Accessing the electric field of light with petahertz bandwidths in ambient air is a rapidly advancing frontier, essential for probing ultrafast dynamics driven by classical or quantum ultrashort pulses. Near-petahertz fieldoscopy has recently demonstrated sub-cycle access to light-matter interactions, enabling label-free spectro-microscopy of liquids and solids with unprecedented spatiotemporal resolution, detection sensitivity, and dynamic range. However, current implementations still rely on temporal scanning and averaging over many laser pulses. Here, we introduce photonic time-stretch fieldoscopy, enabling single-shot electric-field detection at near-petahertz frequencies. Numerical results demonstrate that integrating fieldoscopy with a nonlinear time lens enables the real-time acquisition of ultrashort optical waveforms with a detection bandwidth approaching petahertz. The resulting large temporal aperture and attosecond resolution allow direct single-shot detection of transient electric fields generated in solid or liquid samples. This concept opens new avenues for petahertz electronics, ultrafast spectro-microscopy, and the study of dynamic, non-repetitive optical phenomena
Cryogenic light microscopy of vitrified samples with angstrom precision
Hisham Mazal,
Franz Wieser,
Daniel Bollschweiler,
Vahid Sandoghdar
Proceedings of the National Academy of Sciences of the United States of America
122
e2513583122
(2025)
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High-resolution studies in structural biology are often limited by the challenges of crystallization and low contrast in the cellular native environment. The exquisite labeling specificity of fluorescence microscopy gets around these issues and allows superresolution microscopy, but to date, these works have used chemically fixed samples. To establish light microscopy as a workhorse in structural biology, two main requirements must be fulfilled: near-native sample preservation and near-atomic optical resolution. Here, we introduce single-particle cryogenic light microscopy (spCryo-LM) as a technique that satisfies these key criteria. We adapt established protocols from cryogenic electron microscopy (Cryo-EM) for shock-freezing samples and use a high-vacuum cryogenic shuttle system to transfer them in and out of a liquid-helium cryostat that houses a superresolution fluorescence microscope. By exploiting the enhanced photophysics at low temperature, angstrom precision can be achieved in localizing several fluorophores attached to proteins separated by a few hundred nanometers. We present various characterization studies on vitreous ice, single-molecule photoblinking behavior, and the effects of laser intensity and benchmark our method by resolving the heptameric membrane protein alpha-hemolysin in a synthetic lipid membrane. Additionally, we report on the technique’s capability to resolve membrane proteins in their native cellular membrane environment. spCryo-LM enables structural studies of proteins in their native environment without chemical fixation or protein isolation, and can be integrated with other superresolution or spectroscopic techniques. We believe our approach establishes light microscopy as a powerful tool in structural biology and sets the stage for correlative microscopy with Cryo-EM and related techniques.
Reusability Report: Optimizing T-count in General Quantum Circuits with AlphaTensor-Quantum
Quantum computing has the potential to solve problems that are intractable for classical computers, with possible applications in areas such as drug discovery and high-energy physics. However, the practical implementation of quantum computation is hindered by the complexity of executing quantum circuits on hardware. In particular, minimizing the number of T-gates is crucial for implementing efficient quantum algorithms. AlphaTensor-Quantum is a reinforcement learning-based method designed to optimize the T-count of quantum circuits by formulating the problem as a tensor decomposition task. While it has demonstrated superior performance over existing methods on benchmark quantum arithmetic circuits, its applicability has so far been restricted to specific circuit families, requiring separate, time-intensive training for each new application. This report reproduces some of the key results of the original work and extends AlphaTensor-Quantum's capabilities to simplify random quantum circuits with varying qubit counts, eliminating the need for retraining on new circuits. Our experiments show that a general agent trained on 5- to 8-qubit circuits achieves greater T-count reduction than previous methods for a large fraction of quantum circuits. Furthermore, we demonstrate that a general agent trained on circuits with varying qubit numbers outperforms agents trained on fixed qubit numbers, highlighting the method's generalizability and its potential for broader quantum circuit optimization tasks.
Quantum light drives electrons strongly at metal needle tips
Jonas Heimerl,
Andrei Rasputnyi,
Jonathan Pölloth,
Stefan Meier,
Maria Chekhova,
Peter Hommelhoff
Attosecond science relies on driving photoemitted electrons with the strong optical field of a laser pulse, which represents an intense classical coherent state of light. Bright squeezed vacuum is a quantum state of light that is also intense enough to drive strong-field physics. However, its mean optical electric field is zero, suggesting that, in a semi-classical view, electrons should not experience strong driving. The question arises if and how this quantum state of light can generate signatures of attosecond dynamics in strong-field photoemission. Here we show that the key signatures of strong-field physics—the high energy plateau and subsequent cut-off—also appear under driving of a needle tip by bright squeezed vacuum, but only when we post-select electron energy spectra on the individual photon number of each pulse. When averaging over many shots, we observe broad energy spectra without a plateau. This suggests that electrons driven by bright squeezed vacuum behave as if driven by an ensemble of coherent states of light. Our findings bridge strong-field physics and quantum optics, offering insights into bright squeezed vacuum and other quantum light states, and suggest the use of strongly driven electrons as quantum light sensors.
Ultrafast nonlinear dynamics of indium tin oxide nanocrystals probed via fieldoscopy
Andreas Herbst,
Anchit Srivastava,
Kilian Scheffter,
Soyeon Jun,
Steffen Gommel,
Luca Rebecchi,
Sidharth Kuriyil,
Andrea Rubino,
Nicolo Petrini, et al.
Scalable, high-speed, small-footprint photonic switching platforms are essential for advancing optical communication. An effective optical switch must operate at high duty cycles with fast recovery times, while maintaining substantial modulation depth and full reversibility. Colloidal nanocrystals, such as indium tin oxide (ITO), offer a scalable platform to meet these requirements. In this work, the transmission of ITO nanocrystals near their epsilon-near-zero wavelength is modulated by two-cycle optical pulses at a repetition rate of one megahertz. The modulator exhibits a broad bandwidth spanning from 2 to 2.5 µm. Sensitive fieldoscopy measurements resolve the transient electric-field response of the ITO for the first time, showing that the modulation remains reversible for excitation fluences up to 1.2 mJ cm−2 with a modulation depth of 10%, and becomes fully irreversible beyond 3.3 mJ cm−2, while reaching modulation depth of up to 20%. Field sampling further indicates that at higher excitation fluences, the relative contribution from the first cycle of the optical pulses is reduced. These findings are crucial for the development of all-optical switching, telecommunications, and sensing technologies capable of operating at terahertz switching frequencies.
Selective configuration interaction methods approximate correlated molecular ground- and excited states by considering only the most relevant Slater determinants in the expansion. While a recently proposed neural-network-assisted approach efficiently identifies such determinants, the procedure typically relies on canonical Hartree-Fock orbitals, which are optimized only at the mean-field level. Here we assess approximate natural orbitals - eigenfunctions of the one-particle density matrix computed from intermediate many-body eigenstates - as an alternative. Across our benchmarks for H2O, NH3, CO, and C3H8 we see a consistent reduction in the required determinants for a given accuracy of the computed correlation energy compared to full configuration interaction calculations. Our results confirm that even approximate natural orbitals constitute a simple yet powerful strategy to enhance the efficiency of neural-network-assisted configuration interaction calculations.
Orbital Optimization and Neural-Network-Assisted Configuration Interaction Calculations of Rydberg States
Gianluca Levi,
Max Kroesbergen,
Louis Thirion,
Yorick L. A. Schmerwitz,
Elvar Ö. Jónsson,
Pavlo Bilous,
Philipp Hansmann,
Hannes Jónsson
Rydberg excited states of molecules pose a challenge for electronic structure calculations because of their highly diffuse electron distribution. Even large and elaborate atomic basis sets tend to underrepresent the long-range tail, overly confining the Rydberg state. An approach is presented where the molecular orbitals are variationally optimized for the excited state using a plane wave basis set in Hartree-Fock calculations, followed by configuration interaction calculations on the resulting reference. Using excited state optimized plane wave orbitals greatly enhances the convergence of the many-body calculation, as illustrated by a full configuration interaction calculation of the 2s Rydberg state of H2. A neural-network-based selective configuration interaction approach is then applied to calculations of the 3s, 3px and 3py states of H2O and the 3s and 3pz states of NH3. The obtained values of excitation energy are in close agreement with experimental measurements as well as previous many-body calculations based on sufficiently diffuse atomic basis sets. Previously reported high-level calculations limited to atomic basis sets lacking extra diffuse functions, such as aug-cc-pVTZ, give significantly higher estimates due to confinement of the Rydberg states.
Quantitative phase deformability cytometry (QP-DC) for precise and clinically relevant multiparametric immune cell profiling
Kyoohyun Kim,
Eoghan O’Connell,
Christine Schauer,
Janina Schoen,
Jiwoo Shim,
Florian Mayerle,
Philipp Radler,
Philipp Lebhardt,
Martin Kräter, et al.
bioRxiv 2025.10.27.684875v1
(2025)
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Imaging flow cytometry enables the detailed analysis of cell morphology and internal structures through high-throughput cell imaging, and quantitative phase imaging (QPI)-based microfluidic approaches have extended this by providing label-free measures such as dry mass and refractive index (RI). Building on these developments, we present quantitative phase deformability cytometry (QP-DC), which integrates QPI with deformability cytometry to simultaneously measure morphology, mechanics, and intrinsic biophysical parameters such as mass density and dry mass. Numerical refocusing ensures in-focus images independent of axial position, improving precision in contour detection and feature extraction. Using microspheres and whole blood, we validated QP-DC and then applied it to neutrophils under lipopolysaccharide (LPS) stimulation and from patients with systemic lupus erythematosus (SLE). QP-DC revealed LPS-induced reductions in neutrophil mass density and identified heterogeneous subpopulations in SLE. These results demonstrate the capability of QP-DC for precise biophysical and mechanical characterization, offering significant potential for research and clinical diagnostics.
Turnkey soliton frequency combs from a linear fiber cavity with microresonator mirror
We demonstrate robust turnkey soliton frequency combs using a fiber Fabry-Pérot cavity laser. This believed to be novel linear fiber-laser-based approach leverages Rayleigh scattering from a microresonator to serve as a partial reflector to facilitate both lasing and soliton generation. Numerical simulations are used to optimize the gain-maximum wavelength of the fiber cavity to match the target wavelength, at which the microresonator exhibits strong backscattering. A fused silica microrod resonator with an intrinsic Q-factor of 2 × 108 simultaneously acts as partial reflector and filter for the laser cavity. Based on this fiber cavity laser, we successfully generate soliton crystal frequency combs in a fused silica microresonator with a mode spacing of approximately 109 GHz. We also validate soliton comb generation using different microresonators with varying dimensions. Our laser system exhibits self-injection locking to one of the microresonator modes. Turnkey performance is evaluated through laser current switching tests. The laser power conversion efficiency from 980 nm to 1550 nm is 25%. As a complement to chip-based systems, our work provides insights into soliton generation using extremely low-loss laser cavities and narrow linewidth fused silica microresonators. Our soliton frequency combs are expected to advance various microwave photonic applications that demand long-term stability and turnkey performance.
Biphasic inflammation control by fibroblasts enables spinal cord regeneration in zebrafish
Nora John,
Thomas Fleming,
Julia Kolb,
Olga Lyraki,
Sebastián Vásquez-Sepúlveda,
Asha Parmer,
Kyoohyun Kim,
Maria Tarczewska,
Kanwarpal Singh, et al.
Fibrosis and persistent inflammation are interconnected processes that inhibit axon regeneration in the mammalian central nervous system (CNS). In zebrafish, by contrast, fibroblast-derived extracellular matrix deposition and inflammation are tightly regulated to facilitate regeneration. However, the regulatory cross-talk between fibroblasts and the innate immune system in the regenerating CNS remains poorly understood. Here, we show that zebrafish fibroblasts possess a dual role in inducing and resolving inflammation, which are both essential for regeneration. We identify a transient, injury-specific cthrc1a+ fibroblast state with an inflammation-associated, less differentiated, and non-fibrotic profile. Induction of this fibroblast state precedes and contributes to the initiation of the inflammatory response. At the peak of neutrophil influx, cthrc1a+ fibroblasts coordinate the resolution of inflammation. Disruption of these inflammation dynamics alters the mechano-structural properties of the lesion environment and inhibits axon regeneration. This establishes the biphasic inflammation control by dedifferentiated fibroblasts as a pivotal mechanism for CNS regeneration.
Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
Remmy Zen,
Jan Olle,
Luis Colmenarez,
Matteo Puviani,
Markus Müller,
Florian Marquardt
Physical Review X
15
041012
(2025)
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One of the key aspects in the realization of large-scale fault-tolerant quantum computers is quan- tum error correction (QEC). The first essential step of QEC is to encode the logical state into physical qubits in a fault-tolerant manner. Recently, flag-based protocols have been introduced that use ancillary qubits to flag harmful errors. However, there is no clear recipe for finding a compact quantum circuit with flag-based protocols for fault-tolerant logical state preparation. It is even more difficult when we consider the hardware constraints, such as qubit connectivity and gate set. In this work, we propose and explore reinforcement learning (RL) to automatically discover compact and hardware-adapted quantum circuits that fault-tolerantly prepare the logical state of a QEC code. We show that RL discovers circuits with fewer gates and ancillary qubits than published results without and with hardware constraints of up to 15 physical qubits. Furthermore, RL allows for straightforward exploration of different qubit connectivities and the use of transfer learning to accelerate the discovery. More generally, our work opens the door towards the use of RL for the discovery of fault-tolerant quantum circuits for addressing tasks beyond state preparation, including magic state preparation, logical gate synthesis, or syndrome measurement.
Optical Measurement of the Mass Densitiy of Biological Samples
Mass density is a vital property for the improved biophysical understanding of and within biological samples. It is increasingly attracting active investigations, but still lacks reliable, noncontact techniques to accurately characterize it in biological systems. Contrary to popular belief, refractive index information alone is insufficient to determine a sample’s mass density, as we demonstrate here theoretically and experimentally. Instead, we measured the nonlinear gain of stimulated Brillouin scattering to provide additional information for the mass density estimation. This all-optical method reduces the estimation error 10-fold, offering a more accurate and universal technique for mass density measurements.
Nano-electronvolt Fourier-limited transition of a single surface-adsorbed molecule
Masoud Mirzaei,
Alexey Shkarin,
Burak Gurlek,
Johannes Zirkelbach,
Ashley J. Shin,
Irena Deperasińska,
Boleslaw Kozankiewicz,
Tobias Utikal,
Stephan Götzinger, et al.
High-resolution spectroscopy allows one to probe weak interactions and to detect subtle phenomena. While such measurements are routinely performed on atoms and molecules in the gas phase, spectroscopy of adsorbed species on surfaces is faced with challenges. As a result, previous studies of surface-adsorbed molecules have fallen short of the ultimate resolution, where the transition linewidth is determined by the lifetime of the excited state. In this work, we conceive a new approach to surface deposition and report on Fourier-limited electronic transitions in single dibenzoterrylenes adsorbed onto the surface of an anthracene crystal. By performing spectroscopy and super-resolution microscopy at liquid helium temperature, we shed light on various properties of the adsorbed molecules. Our experimental results pave the way for a new class of experiments in surface science, where high spatial and spectral resolution can be combined.
Fano interference of photon pairs from a metasurface
Jiho Noh,
Tomás Santiago-Cruz,
Chloe F. Doiron,
Hyunseung Jung,
Jaeyeon Yu,
Sadhvikas J. Addamane,
Maria Chekhova,
Igal Brener
Two-photon interference, a quantum phenomenon arising from the principle of indistinguishability, is a powerful tool for quantum state engineering and plays a fundamental role in various quantum technologies. These technologies demand robust and efficient sources of quantum light, as well as scalable, integrable, and multifunctional platforms. In this regard, quantum optical metasurfaces (QOMs) are emerging as promising platforms for the generation and engineering of quantum light, in particular pairs of entangled photons (biphotons) via spontaneous parametric down-conversion (SPDC). Due to the relaxation of the phase-matching condition, SPDC in QOMs allows different channels of biphoton generation, such as those supported by overlapping resonances, to occur simultaneously. In previously reported QOMs, however, SPDC was too weak to observe such effects. Here, we develop QOMs based on [110]-oriented GaAs that provide an order-of-magnitude enhancement in SPDC rate, after accounting for the spectral bandwidth, compared to any other QOMs studied to date. This boosted efficiency allows the QOMs to support the simultaneous generation of SPDC from several spectrally overlapping optical modes. Using a linear polarizer, we intentionally erase the distinguishability between the biphotons from a high-Q quasi-bound-state-in-the-continuum resonance and a low-Q Mie resonance, which results in the first-time observation of two-photon interference, shown in the form of a Fano contour, in the spectrum of biphotons. This quantum interference can enrich the generation of entangled photons in metasurfaces. Their advanced multifunctionality, improved nonlinear response, ease of fabrication, and compact footprint of [110]-GaAs QOMs position them as promising platforms to fulfill the requirements of photonic quantum technologies.
Brillouin-Mandelstam Scattering-based Cooling of Traveling Acoustic Waves from Cryogenic Temperatures
Lisa Fischer,
Laura Blázquez Martínez,
Robin Chenivière,
Johann Troles,
Birgit Stiller
Thermal phonons are a major source of decoherence in quantum mechanical systems. Operating in the quantum ground state is therefore often an experimental prerequisite. Additionally to passive cooling in a cryogenic environment, active laser cooling enables the reduction of phonons at specific acoustic frequencies. Brillouin cooling has been used to show efficient reduction of the thermal phonon population in waveguides at GHz frequencies down to 74 K. In this letter, we demonstrate cooling of a 7.608 GHz acoustic mode by combining Brillouin active cooling with precooling from 77 K using liquid nitrogen. We show a 69 % reduction in the phonon population, resulting in a final temperature of 24.3 K, 50 K lower than previously reported.
Automated discovery of high-dimensional multipartite entanglement with photons that never interacted
Quantum entanglement across spatially separated network nodes is conventionally established through the distribution of photons from a common source or via entanglement swapping that relies on Bell-state measurements and pre-shared entanglement. Path identity, where the emission origins of photons from different sources are made indistinguishable, offers an alternative route. We show that this mechanism enables complex multipartite, high-dimensional, and even logical entanglement between remote nodes whose photons never interacted. Our schemes require neither direct photon interaction, pre-shared entanglement, nor Bell-state measurements, highlighting a distinct resource for distributed quantum communication and computation. All of the solutions were discovered automatically using highly efficient computational design tools, indicating the potential for scientific inspiration from computational algorithms.
Automation and improvement of WBC mechanical profiling in deformability cytometry
Sara Kaliman,
Shada Abuhattum Hofemeier,
Benedikt Hartmann,
Jochen Guck
Deformability cytometry (DC) is a powerful biophysical technique that enables cost-effective, high-throughput characterization of disease-associated changes in blood cell mechanics. Mechanical profiling of living white blood cells (WBCs) is particularly valuable due to their critical role in the immune response. However, reliably identifying and classifying WBC subtypes in a label-free manner remains a significant challenge. Until now, the analysis pipeline has relied on manual gating by trained experts, limiting scalability and reproducibility. In this study, we present a fully automated and generalizable framework for WBC classification in shear flow DC experiments, based on box filters and unsupervised clustering of cell populations. Both box filters and unsupervised clustering rely on cell shape features derived from high-accuracy segmentation and on cell texture features derived from bright-field images. This unsupervised approach not only improves reproducibility and reduces processing time but also overcomes key limitations of supervised models that require extensive training data and often suffer from reduced performance under varying imaging conditions. We validated our method by comparing cell features obtained through manual gating and automated classification across six experimental sets. These sets incorporated variations in blood donors, anticoagulants (EDTA and citrate), blood collection sources (capillary and venous), and device brightness settings. Each set included five repeated measurements. The results consistently confirmed the reliability and robustness of the method across all tested conditions and WBC types. Importantly, this automated pipeline enables the inclusion of WBCs with membrane protrusions—typically excluded from standard analyses—allowing for morphological characterization of potentially activated cells. Moreover, by using shape features derived from the original contour rather than the convex hull, we improve morphological accuracy and reduce measurement variability. This approach thus enhances the accuracy, consistency, and scalability of WBC mechanophenotyping and enables high-throughput analysis across large cohorts.
High-Throughput Mechanomic Screening Reveals Novel Regulators of Single-Cell Mechanics
Laura Strampe,
Katarzyna Plak,
Christine Schweitzer,
Cornelia Liebers,
Paul Müller,
Marta Urbanska,
Martin Kräter,
Buzz Baum,
Jona Kayser, et al.
The mechanical properties of cells are dynamic, allowing them to adjust to different needs in different biological contexts. In recent years, advanced biophysical techniques have enabled the rapid, high-throughput assessment of single-cell mechanics, providing new insights into the regulation of the mechanical cell phenotype. However, the molecular mechanisms by which cells maintain and regulate their mechanical properties remain poorly understood. Here, we present a genome-scale RNA interference (RNAi) screen investigating the roles of kinase and phosphatase genes in regulating single-cell mechanics using Real-Time Fluorescence and Deformability Cytometry (RT-FDC). Our screen identified 82 known and novel mechanical regulators across diverse cellular functions from 214 targeted genes, leveraging RT-FDC’s unique capabilities for comprehensive, high-throughput mechanical phenotyping with single-cell and cell cycle resolution. These findings refine our understanding of how signaling pathways coordinate structural determinants of cell mechanical phenotypes and provide a starting point for uncovering new molecular targets involved in biomechanical regulation across diverse biological systems.
The process of scientific discovery relies on an interplay of observations, analysis, and hypothesis generation. Machine learning is increasingly being adopted to address individual aspects of this process. However, it remains an open challenge to fully automate the open-ended, heuristic, iterative loop required to discover the laws of an unknown system by exploring it through experiments and analysis, without tailoring the approach to the specifics of a given task. Here, we introduce SciExplorer, an agent that leverages large language model tool-use capabilities to enable free-form exploration of systems without any domain-specific blueprints, and apply it to the exploration of physical systems that are initially unknown to the agent. We test SciExplorer on a broad set of models spanning mechanical dynamical systems, wave evolution, and quantum many-body physics. Despite using a minimal set of tools, primarily based on code execution, we observe impressive performance on tasks such as recovering equations of motion from observed dynamics and inferring Hamiltonians from expectation values. The demonstrated effectiveness of this setup opens the door towards similar scientific exploration in other domains, without the need for finetuning or task-specific instructions.
Automated Discovery of Gadgets in Quantum Circuits for Efficient Reinforcement Learning
Reinforcement learning (RL) has proven itself as a powerful tool for the discovery of quantum circuits and quantum protocols. We have recently shown that including composite quantum gates -- referred to as ``gadgets'' -- in the action space of RL agents substantially enhances the RL performance in the context of quantum error correction. However, up to now the gadgets themselves had to be constructed manually. In this paper, we suggest an algorithm for the automated discovery of new gadgets and families of related gadgets. The algorithm is based on the representation of quantum circuits as directed graphs and an automated search for repeated subgraphs. The latter are identified as gadgets. We demonstrate the efficiency of the algorithm, which allows us to find two new gadget families suitable for RL. We compare the performance of 4-qubit gadgets taken from a previously known and a newly discovered family and discuss their advantages and disadvantages.
Efficient Raman shifting of microjoule pulses in N2-filled anti-resonant fiber
Yishai Eisenberg,
Yi-Hao Chen,
Wenchao Wang,
Francesco Tani,
Michael Frosz,
Jeffrey Moses,
Chris Xu,
Frank W. Wise
Journal of the Optical Society of America B-Optical Physics
42
2291-2295
(2025)
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We demonstrate efficient frequency down-conversion of femtosecond pulses based on the interplay of Raman-enhanced self-phase modulation and impulsive redshifting in gas-filled anti-resonant hollow-core fiber. With 20 µJ and 140 fs pulses at 1030 nm launched into a short length of fiber, pulses with durations below 100 fs are generated between 1100 and 1300 nm, with over 26% efficiency and above 5 µJ energy, for peak powers between 50 and 100 MW. The modest experimental requirements and highly efficient conversion make this a practical source for wavelength-specific applications.
Optical frequency shifter based on continuous-wave pump fields
Anica Hamer,
Frank Vewinger,
Michael Frosz,
Simon Stellmer
Practical implementations of quantum information networks require frequency conversion of individual photons. Approaches based on a molecular gas as the nonlinear medium cover a wide range of the optical spectrum and promise high efficiency at negligible background. We present polarization-preserving frequency conversion in a hydrogen-loaded hollow-core fiber using continuous-wave pump fields. We demonstrate conversion efficiency at the level of a few per mille, discuss various limitations and loss mechanisms, and present a route to increase conversion efficiency to near unity.
Unifying framework for non-Hermitian and Hermitian topology in driven-dissipative systems
Recently, a one-to-one correspondence between non-trivial non-Hermitian topology and directional amplification has been demonstrated, theoretically and experimentally, for the case of one complex band. Here, we extend our framework to multiple bands and higher spatial dimension. This proves to be far from trivial. Building on the singular value decomposition, we introduce a new quantity that we dub generalised singular spectrum (GSS). The GSS allows us to define physically meaningful bands related to the system's scattering behaviour and to define invariants for novel notions of point gaps (non-Hermitian topology) and line gaps (Hermitian-like topology), respectively. For both invariants, we prove a bulk-boundary correspondence and show that they give rise to two different kinds of topological edge modes. We illustrate our results with a 1D non-Hermitian Su-Schrieffer-Heeger (SSH) model and a 2D non-Hermitian model that features corner-to-corner amplification. Our work is relevant for many state-of-the-art experimental platforms and it sets the stage for applications such as novel directional amplifiers and non-reciprocal sensors.
Universality Classes of delocalization-localization transitions in Chiral Symplectic Class
By a simulation study of three-dimensional (3D) and two-dimensional (2D) disordered lattice models in the chiral symplectic class, we show that one-dimensional (1D) weak topology universally induces an intermediate quasi-localized (QL) phase between metal and Anderson-localized phases, in which the localization length of wave functions is divergent only along the spatial direction associated with the weak topological index. Our numerical evaluation of the critical exponents of the metal-to-QL transition and the Anderson transition (in the absence of the weak topology) demonstrates that they belong to different universality classes. We also confirm that the critical exponents of these two transitions in the chiral symplectic class significantly differ from those in the chiral unitary and chiral orthogonal classes, highlighting the impact of Kramers time-reversal symmetry on quantum critical behavior.
Optofluidic Waveguides for the Label-Free Study of Silk Protein Aggregates
Jan R. Heck,
Zenon Toprakcioglu,
Tobias E. Naegele,
Michael Frosz,
Tuomas P. J. Knowles,
Tijmen G. Euser
Methods for studying protein aggregation are crucial to understanding the associated disease pathologies and for functional biomaterial synthesis in nature and in the laboratory. The ideal measurement platform is low-volume, label-free, and noncontact, as well as easily integrated into continuous-flow microfluidic experiments to provide scalability. Current approaches realize only a subset of these requirements. Here, we demonstrate a new technique for studying protein aggregates and in situ aggregation within hollow-core photonic crystal fibers. These optofluidic waveguides allow us to perform continuous-flow microfluidic label-free analysis of silk fibroin protein in the form of preformed nanofibrillar aggregates and on the native protein as it undergoes aggregation in situ in the optofluidic waveguide. We demonstrate label-free ultraviolet absorbance measurements on both calibration-standard nanospheres and silk fibroin aggregates as well as monitoring the aggregation of native silk fibroin protein solution via simultaneous ultraviolet absorbance and intrinsic fluorescence measurements in situ. This technique forms a platform for the study of protein aggregation that is low volume, label-free, and optical, thereby providing a valuable optofluidic tool for a range of protein biophysics.
Multiple many-body localization transitions in a driven non-Hermitian quasiperiodic chain
Sanchayan Banerjee,
Ayan Banerjee,
Tapan Mishra,
Flore K. Kunst
We investigate the fate of a many-body localized phase in a non-Hermitian quasiperiodic model of hardcore bosons subjected to periodic driving. While in general, the many-body localized system is known to thermalize with increasing driving period due to Floquet heating, in this case, we demonstrate that the initially localized system first delocalizes and then localizes again, resulting in a re-entrant many-body localization (MBL) transition as a function of the driving period. Strikingly, further increase in the driving period results in a series of localization-delocalization transitions leaving behind traces of extended regimes (islands) in between MBL phases. Furthermore, non-Hermiticity renders the extended islands boundary-sensitive, resulting in a Floquet many-body skin effect under open boundaries. We present numerical evidence from spectral and dynamic studies, confirming these findings. Our study opens new pathways for understanding the interplay between non-Hermiticity and quasiperiodicity in driven systems.
Three-dimensional ghost-free representations of the Pais-Uhlenbeck model from Tri-Hamiltonians
We present a detailed analysis of the sixth-order Pais-Uhlenbeck oscillator and construct three-dimensional ghost-free representations through a Tri-Hamiltonian framework. We identify a six-dimensional Abelian Lie algebra of the PU model's dynamical flow and derive a hierarchy of conserved Hamiltonians governed by multiple compatible Poisson structures. These structures enable the realisation of a complete Tri-Hamiltonian formulation that generates identical dynamical flows. Positive-definite Hamiltonians are constructed, and their relation to the full Tri-Hamiltonian hierarchy is analysed. Furthermore, we develop a mapping between the PU model and a class of three-dimensional coupled second-order systems, revealing explicit conditions for ghost-free equivalence. We also explore the consequences of introducing interaction terms, showing that the multi-Hamiltonian structure is generally lost in such cases.
Abelian spectral topology of multifold exceptional points
Marcus Stålhammar,
Lukas Rødland
Physical Review Research
7
033246
(2025)
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The advent of non-Hermitian physics has enriched the plethora of topological phases to include phenomena without Hermitian counterparts. Despite being among the most well-studied uniquely non-Hermitian features, the topological properties of multifold exceptional points, n-fold spectral degeneracies (EPns) at which also the corresponding eigenvectors coalesce, were only recently revealed in terms of topological resultant winding numbers and concomitant Abelian doubling theorems. Nevertheless, a more mathematically fundamental description of EPns and their topological nature has remained an open question. To fill this void, in this article, we revisit the topological classification of EPns in generic systems and systems with local symmetries, generalize it in terms of more mathematically tractable (local) similarity relations, and extend it to include all such similarities as well as nonlocal symmetries. Through the resultant vector, whose components are given in terms of the resultants between the corresponding characteristic polynomial and its derivatives, the topological nature of the resultant winding number is understood in several ways: in terms of (1) the tenfold classification of Hermitian topological matter, (2) the framework of Mayer-Vietoris sequence, and (3) the classification of vector bundles. The classification scheme further predicts the existence of topological bulk Fermi arcs protected by a ℤ2-invariant, induced by nonlocal symmetries, dubbed ℤ2-protected Fermi arcs. Our work reveals the mathematical foundations on which the topological nature of EPns resides, enriches the theoretical understanding of non-Hermitian spectral features, and will therefore find great use in modern experiments within both classical and quantum physics.
Although micron-sized microgels have become important building blocks in regenerative materials, offering decisive interactions with living matter, their chemical composition mostly significantly varies when their network morphology is tuned. Since cell behavior is simultaneously affected by the physical, chemical, and structural properties of the gel network, microgels with variable morphology but chemical equivalence are of interest. This work describes a new method to produce thermoresponsive microgels with defined mechanical properties, surface morphologies, and volume phase transition temperatures. A wide variety of microgels is synthesized by crosslinking monomers or star polymers at different temperatures using thermally assisted microfluidics. The diversification of microgels with different network structures and morphologies but of chemical equivalence offers a new platform of microgel building blocks with the ability to undergo phase transition at physiological temperatures. The method holds high potential to create soft and dynamic materials while maintaining the chemical composition for a wide variety of applications in biomedicine.
Red Blood Cell-derived Extracellular Vesicles as biomaterials: the opportunity of freezing-induced accelerated aging
Lucia Paolini,
Miriam Romano,
Valentina Mangolini,
Selene Tassoni,
Shuhan Jiang,
Elena Laura Mazzoldi,
Angelo Musicò,
Andrea Zendrini,
Anna Kashkanova, et al.
Red blood cell-derived extracellular vesicles (RBC-EVs) are emerging as promising biomaterials for next-generation drug delivery, owing to their intrinsic biocompatibility, immune evasion properties, and minimal oncogenic risk. However, their broader application is currently limited by unresolved challenges related to heterogeneity, reproducibility, and long-term storage stability. By combining discontinuous sucrose density gradient separation with high-resolution interferometric nanoparticle tracking analysis, we identified a sharp bimodal size distribution of the vesicles in freshly prepared samples. We then tracked how long-term storage at −80 °C drove its conversion into a monomodal distribution. To reproduce these conditions in a shorter time frame, we developed an “accelerated-ageing” protocol based on freeze–thaw cycles that generates RBC-EV samples with homogeneous density, size distribution, and biological activity, effectively replicating the properties of preparations stored for six months at −80 °C. This new vesicle population results stable and retains membrane integrity and cellular internalization capacity, as confirmed by surface-associated enzymatic activity assays and uptake tests in cancer cell lines. These results suggest that freezing-induced “accelerated ageing” represents an effective method for the optimization and standardization of RBC-EVs as building blocks for biomaterial and bioengineering applications.
Training of physical neural networks
Ali Momeni,
Babak Rahmani,
Benjamin Scellier,
Logan G. Wright,
Peter L. McMahon,
Clara C. Wanjura,
Yuhang Li,
Anas Skalli,
Natalia G. Berloff, et al.
Physical neural networks (PNNs) are a class of neural-like networks that make use of analogue physical systems to perform computations. Although at present confined to small-scale laboratory demonstrations, PNNs could one day transform how artificial intelligence (AI) calculations are performed. Could we train AI models many orders of magnitude larger than present ones? Could we perform model inference locally and privately on edge devices? Research over the past few years has shown that the answer to these questions is probably “yes, with enough research”. Because PNNs can make use of analogue physical computations more directly, flexibly and opportunistically than traditional computing hardware, they could change what is possible and practical for AI systems. To do this, however, will require notable progress, rethinking both how AI models work and how they are trained—primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs, backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs and, so far, no method has been shown to scale to large models with the same performance as the backpropagation algorithm widely used in deep learning today. However, this challenge has been rapidly changing and a diverse ecosystem of training techniques provides clues for how PNNs may one day be used to create both more efficient and larger-scale realizations of present-scale AI models.
A label-free method for measuring the composition of multicomponent biomolecular condensates
Patrick M. McCall,
Kyoohyun Kim,
Anna Shevchenko,
Martine Ruer-Gruß,
Jan Peychl,
Jochen Guck,
Andrej Shevchenko,
Anthony A. Hyman,
Jan Brugués
Many subcellular compartments are biomolecular condensates made of multiple components, often including several distinct proteins and nucleic acids. However, current tools to measure condensate composition are limited and cannot capture this complexity quantitatively because they either require fluorescent labels, which can perturb composition, or can distinguish only one or two components. Here we describe a label-free method based on quantitative phase imaging and analysis of tie-lines and refractive index to measure the composition of reconstituted condensates with multiple components. We first validate the method empirically in binary mixtures, revealing sequence-encoded density variation and complex ageing dynamics for condensates composed of full-length proteins. We then use analysis of tie-lines and refractive index to simultaneously resolve the concentrations of five macromolecular solutes in multicomponent condensates containing RNA and constructs of multiple RNA-binding proteins. Our measurements reveal an unexpected decoupling of density and composition, highlighting the need to determine molecular stoichiometry in multicomponent condensates. We foresee this approach enabling the study of compositional regulation of condensate properties and function.
Bridging the Digital Divide
Hanieh Fattahi,
Asghar Ghorbani
Optics and Photonics News
September 2025
(2025)
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Limited infrastructure, scarce educational resources, and unreliable internet access often hinder physics and photonics education in underdeveloped regions. These barriers create deep inequities in Science, Technology, Engineering, and Mathematics (STEM) education. This article explores how Small Language Models (SLMs)-compact, AI-powered tools that can run offline on low-power devices, offering a scalable solution. By acting as virtual tutors, enabling native-language instruction, and supporting interactive learning, SLMs can help address the shortage of trained educators and laboratory access. By narrowing the digital divide through targeted investment in AI technologies, SLMs present a scalable and inclusive solution to advance STEM education and foster scientific empowerment in marginalized communities.
Direct, high-throughput linking of single cell imaging and gene expression
Catherine Xu,
Georg Meisl,
Nikita Moshkov,
Karolis Goda,
Alexey Shkarin,
Maximilian Schlögel,
Tuomas PJ Knowles,
Linas Mazutis,
Jochen Guck
bioRxiv 10.1101/2025.09.01.672798
(2025)
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Single-cell technologies have transformed our ability to dissect cellular heterogeneity by enabling measurements of individual molecular modalities, from genome and transcriptome to proteome and metabolome. However, at the single-cell level, the physical properties of cells, such as size, morphology, and mechanical state, remain largely disconnected from molecular profiling, limiting our understanding of the relationship between these aspects of cellular phenotype and gene expression. We introduce im-seq, a high-throughput, flow-based platform that integrates live-cell imaging with droplet-based mRNA sequencing at the single-cell level. By optically barcoding individual cells, im-seq enables the joint capture of physical and transcriptional profiles from single cells. We demonstrate that this multimodal approach can resolve physical and molecular features across cell lines, to reveal genes associated with phenotypic properties at unprecedented resolution. Our results establish im-seq as a versatile high-throughput framework for linking genetic information to physical properties, providing the large scale, information-dense datasets needed to power the next generation of data-driven discoveries in cell biology.
Bottom-up investigation of spatiotemporal glycocalyx dynamics with interferometric scattering microscopy
Carla M. Brunner,
Lorenz Pietsch,
Ingo vom Sondern,
Michael Röhrl,
Cristian Popov,
Marius F.W. Trollmann,
Richard W. Taylor,
Martin Blessing,
Cornelia Holler, et al.
Journal of the American Chemical Society
147
32799-32808
(2025)
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Over recent decades, the glycocalyx, an extracellular organelle comprised of a multitude of glycolipids, glycoproteins, proteoglycans and glycoRNA, has gained considerable interest in cellular biology. While research in this field has revealed its tremendous importance in evermore aspects of physiological and pathological cellular processes, many of the principles that govern the role of the glycocalyx in these processes on a molecular level are still unknown. In order to unravel the fundamental laws underlying glycocalyx function, new technologies are required that enable the distinction between individual subprocesses within the intricate environment of the glycocalyx. Here, we establish an experimental platform to investigate the dynamics of the glycocalyx at the nanometer and microsecond length and time scales in a bottom-up fashion. We synthesized defined model glycans and installed them on supported lipid bilayers, assembling glycocalyx model systems with tunable properties. By investigating these tunable model systems with interferometric scattering (iSCAT) microscopy, we gain access to the required spatiotemporal resolution. We found a strong correlation between the molecular structure of several investigated model glycans and global dynamics of the system. Our findings are corroborated by atomistic and coarsegrained molecular dynamics simulations. Our results provide the first direct experimental evidence on the relationship between glycan structure, organization, and dynamics, offering a robust and versatile basis for a quantitative understanding of glycocalyx biology and physics at the molecular level.
Cryo–light microscopy with angstrom precision deciphers structural conformations of PIEZO1 in its native state
Hisham Mazal,
Franz Wieser,
Daniel Bollschweiler,
Alexandra Schambony,
Vahid Sandoghdar
Science Advances
11
eadw4402
(2025)
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Investigations based on cryo–electron microscopy (cryo-EM), atomic force microscopy, and super-resolution microscopy reveal a symmetric trimer with propeller-like blades for the mechanosensitive ion channel PIEZO. However, a conclusive understanding of its conformations in the cell membrane is lacking. Here, we implement a high-vacuum cryogenic shuttle to transfer shock-frozen cell membranes in and out of a cryostat designed for single-particle cryo–light microscopy (spCryo-LM). By localizing fluorescent labels placed at the extremities of the blades of the mouse PIEZO1 protein in unroofed cell membranes, we ascertain three configurations with radii of 6, 12, and 20 nanometers as projected onto the membrane plane. We elaborate on the correspondence of these data with previous reports in the literature. The combination of spCryo-LM with cryo-EM promises to provide quantitative insights into the structure and function of biomolecular complexes in their native environments without the need for chemical fixation or protein isolation, ushering in a new regime of correlative studies in structural biology.
Measuring Molecular Mass Densities at Subcellular Resolution Using Optical Diffraction Tomography
Kyoohyun Kim,
Abin Biswas,
Jochen Guck,
Simone Reber
The Nuclear Membrane. Methods in Molecular Biology
119-141
(2025)
| Journal
Biological systems intricately regulate their density and volume throughout their life cycles and in response to physiological changes. Mass density, as a fundamental physical quantity, plays significant roles in biological processes such as differentiation, cell growth, protein synthesis, and condensate formation. Loss of density homeostasis on the other hand can have severe consequences including cellular senescence and disease states. Recent developments in biophotonics now enable high-resolution density quantification, providing new insights into the biophysical properties of cells and subcellular structures. One such technique is optical diffraction tomography (ODT), which offers label-free, high-resolution measurements of mass density distribution based on refractive index (RI) measurements. In this chapter, we present a comprehensive guide to implementing ODT for quantitative characterization of mass density distribution in biological systems, including in vivo (adherent cell culture) and in vitro (Xenopus egg extract) samples. We begin by detailing the optical setups, emphasizing key considerations for optimizing tomography acquisition. Subsequently, we introduce preparation protocols tailored to biological samples in various types of sample carriers and offer guidance on standard image acquisition and data analysis procedures. Finally, we address the challenges posed by the linear relationship between RI and mass density in complex substances, offering strategies for overcoming these limitations.
Artificial discovery of lattice models for wave transport
Jonas Landgraf,
Clara C. Wanjura,
Vittorio Peano,
Florian Marquardt
Wave transport devices, such as amplifiers, frequency converters, and nonreciprocal devices, are essential for modern communication, signal processing, and sensing applications. Of particular interest are traveling wave setups, which offer excellent gain and bandwidth properties. So far, the conceptual design of those devices has relied on human ingenuity. This makes it difficult and time-consuming to explore the full design space under a variety of constraints and target functionalities. In our work, we present a method which automates this challenge. By optimizing the discrete and continuous parameters of periodic coupled-mode lattices, our approach identifies the simplest lattices that achieve the target transport functionality, and we apply it to discover new schemes for directional amplifiers, isolators, and frequency demultiplexers. Leveraging automated symbolic regression tools, we find closed analytical expressions that facilitate the discovery of generalizable construction rules. Moreover, we utilize important conceptual connections between the device transport properties and non-Hermitian topology. The resulting structures can be implemented on a variety of platforms, including microwave, optical, and optomechanical systems. Our approach opens the door to extensions like the artificial discovery of lattice models with desired properties in higher dimensions or with nonlinear interactions.
Training nonlinear optical neural networks with Scattering Backpropagation
Nicola Dal Cin,
Florian Marquardt,
Clara C. Wanjura
As deep learning applications continue to deploy increasingly large artificial neural networks, the associated high energy demands are creating a need for alternative neuromorphic approaches. Optics and photonics are particularly compelling platforms as they offer high speeds and energy efficiency. Neuromorphic systems based on nonlinear optics promise high expressivity with a minimal number of parameters. However, so far, there is no efficient and generic physics-based training method allowing us to extract gradients for the most general class of nonlinear optical systems. In this work, we present Scattering Backpropagation, an efficient method for experimentally measuring approximated gradients for nonlinear optical neural networks. Remarkably, our approach does not require a mathematical model of the physical nonlinearity, and only involves two scattering experiments to extract all gradient approximations. The estimation precision depends on the deviation from reciprocity. We successfully apply our method to well-known benchmarks such as XOR and MNIST. Scattering Backpropagation is widely applicable to existing state-of-the-art, scalable platforms, such as optics, microwave, and also extends to other physical platforms such as electrical circuits.
Conserved nucleocytoplasmic density homeostasis drives cellular organization across euraryotes
Abin Biswas,
Omar Muñoz,
Kyoohyun Kim,
Carsten Hoege,
Benjamin M. Lorton,
Rainer Nikolay,
Matthew L. Kraushar,
David Shechter,
Jochen Guck, et al.
Nature Communications
16
7597
(2025)
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The confinement of macromolecules has profound implications for cellular biochemistry. It generates environments with specific physical properties affecting diffusion, macromolecular crowding, and reaction rates. Yet, it remains unknown how intracellular density distributions emerge and affect cellular physiology. Here, we show that the nucleus is less dense than the cytoplasm and that living systems establish a conserved density ratio between these compartments due to a pressure balance across the nuclear envelope. Nuclear transport establishes a specific nuclear proteome that exerts a colloid osmotic pressure, which, assisted by chromatin pressure, increases nuclear volume. During C. elegans development, the nuclear-to-cytoplasmic density ratio is robustly maintained even when nuclear-to-cytoplasmic volume ratios change. We show that loss of density homeostasis correlates with altered cell functions like senescence and propose density distributions as key markers in pathophysiology. In summary, this study reveals a homeostatic coupling of macromolecular densities that drives cellular organization and function.
Magnetic tunnel junctions driven by hybrid optical-electrical signals as a flexible neuromorphic computing platform
Felix Oberbauer,
Tristan Joachim Winkel,
Tim Böhnert,
Clara C. Wanjura,
Marcel S. Claro,
Luana Benetti,
Ihsan Çaha,
Francis Leonard Deepak,
Farshad Moradi, et al.
Communications Physics
8
329
(2025)
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Magnetic tunnel junctions (MTJs) offer a promising pathway toward energy-efficient neuromorphic computing due to their nanoscale footprint, nonvolatile switching, and intrinsic nonlinear dynamics that emulate synaptic behavior. However, generating large thermoelectric voltages with bias-tunable nonlinearities for neuromorphic use remains largely unexplored. Here, we introduce a hybrid opto-electrical excitation scheme—combining pulsed laser heating with DC bias—to drive MTJs into the nonlinear bias-enhanced tunnel magneto-Seebeck regime. This regime yields thermoelectric voltages in the tens of millivolts with a strong contrast between magnetic states, while also revealing spiking and double-switching behavior linked to vortex dynamics and fixed-layer depinning. The thermovoltage exhibits cubic dependence on bias current, enabling tunable synaptic weights. We simulate a single-layer neuromorphic network using optically encoded inputs and achieve 93.7% classification accuracy on handwritten digits. These results establish hybrid-driven MTJs as a compact, CMOS-compatible platform for neuromorphic computing, integrating optical input with spintronic functionality.
Studying thermal radiation withTmatrices
Juan Diego Mazo-Vásquez,
Markus Nyman,
Marjan Krstić,
Lukas Rebholz,
Carsten Rockstuhl,
Ivan Fernandez-Corbaton
Physical Review B
112
054307
(2025)
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We introduce a basic formalism for computing thermal radiation by combining Waterman's T-matrix method with an algebraic approach to light-matter interactions. The formalism applies to nanoparticles, clusters thereof, and also molecules. In exemplary applications, we explore how a chiral structure can induce an imbalance in the circular polarization of thermal radiation. While the imbalance is rather small for a chiral molecule such as R-BINOL, a much larger imbalance is observed for an optimized silver helix of approximately 200 nm in size. Besides the directional Kirchhoff law used in this article, the formalism is suitable for implementing more nuanced theories and also provides a straightforward path to the computation of thermal radiation spectra of astronomical objects moving at relativistic speeds with respect to the measurement devices.
Exploring the role of polarization in fiber-based quantum sources
Carla M. Brunner,
Nicolas Y. Joly
Optics Express
33
34756-34771
(2025)
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Optical fibers constitute an attractive platform for the realization of nonlinear and quantum optics processes. Here we show, through theoretical considerations, how polarization effects of both third-order parametric down-conversion and four-wave-mixing in optical fibers may be exploited to enhance detection schemes. We apply our general framework specifically to the case of tapered fibers for photon triplet generation, a long-standing goal within quantum optics, and obtain explicit expectation values for its efficiency. A quantitative investigation of four-wave-mixing in a microstructured solid-core fiber provides significant consequences for the role of polarization in experimental design.
The analytically tractable zoo of similarity-induced exceptional structures
Anton Montag,
Jordan Isaacs,
Marcus Stålhammar,
Flore K. Kunst
Exceptional points (EPs) are non-Hermitian spectral degeneracies marking a simultaneous coalescence of eigenvalues and eigenvectors. Despite the fact that multiband n-fold EPs (EPns) generically emerge as special points on manifolds of EPms, where m<n, EPns as well as their topological properties have hitherto been studied as isolated objects. In this work we address this issue and carefully map out the emerging properties of multifold exceptional structures in three and four dimensions under the influence of one or multiple generalized similarities, revealing diverse combinations of EPms in direct connection to EPns. We find that simply counting the number of constraints defining the EPns is not sufficient in the presence of similarities; the constraints can also be satisfied by the EPm-manifolds obeying certain spectral symmetries in the complex eigenvalue plane, reducing their dimension beyond what is expected from counting the number of constraints. Furthermore, the induced spectral symmetries not always allow for any EPm-manifold to emerge in n-band systems, making the plethora of exceptional structures deviate further from naive expectations. We illustrate our findings in simple periodic toy models. By relying on similarity relations instead of the less general symmetries, we simultaneously cover several physically relevant scenarios, ranging from optics and topolectrical circuits, to open quantum systems. This makes our predictions highly relevant and broadly applicable in modern research, as well as experimentally viable within various branches of physics.
Imaging single ion channels via their Rayleigh scattering
The fast and convenient study of ion channels in cells continues to pose challenges. Interferometric scattering microscopy delivers robust signals from single channels, paving the way for label-free investigation of their function in live cells.
Long-lived cellular molecules in the brain
Martin W. Hetzer,
Tomohisha Toda
Trends in Neurosciences
48
645-654
(2025)
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In long-lived mammals, including humans, brain cell homeostasis is critical for maintaining brain function throughout life. Most neurons are generated during development and must maintain their cellular identity and plasticity to preserve brain function. Although extensive studies indicate the importance of recycling and regenerating cellular molecules to maintain cellular homeostasis, recent evidence has shown that some proteins and RNAs do not turn over for months and even years. We propose that these long-lived cellular molecules may be the basis for maintaining brain function in the long term, but also a potential convergent target of brain aging. We highlight key discoveries and challenges, and propose potential directions to unravel the mystery of brain cell longevity.
Hybridization of molecules via a common photonic mode
Jahangir Nobakht,
André Pscherer,
Jan Renger,
Stephan Götzinger,
Vahid Sandoghdar
Proceedings of the National Academy of Sciences of the United States of America
122
e2505161122
(2025)
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Atoms and molecules usually hybridize and form bonds when they come in very close<br>proximity of each other. In this work, we show that molecules can hybridize even<br>through far-field electromagnetic interactions mediated by the shared mode of an<br>optical microcavity. We discuss a collective enhancement of the vacuum Rabi splitting<br>and study super- and subradiant states that arise from the cavity-mediated coupling<br>both in the resonant and dispersive regimes. Moreover, we demonstrate a two-photon<br>transition that emerges between the ground and excited states of the new optical<br>compound. Our experimental data are in excellent agreement with the predictions<br>of the Tavis–Cummings Hamiltonian and open the door to the realization of hybrid<br>light–matter materials.
All experimental evidence {indicates} that the vacuum is not void, but filled with something truly quantum. This is reflected by terms such as {zero-point} fluctuations, and Dirac's sea of virtual particle-antiparticle pairs, and last but not least the vacuum is the medium responsible for Maxwell's displacement current. While quantum electrodynamics (QED) is an exceptionally successful theory, it remains a perturbative framework rather than a fully self-contained one. Inherently, it includes singularities and divergences, which prevent the precise calculation of fundamental quantities such as the fine-structure constant $α$. Any direct attempt to compute $α$ results in divergent values. However, and most remarkable, what can be determined is how $α$ ``runs", meaning how it varies with energy or exchanged momentum. In this article, we review the historical development of these ideas, the current state of knowledge, and ongoing efforts to find ways to move further. This includes a simple model to describe vacuum polarization in the low-energy regime, when considering only small (linear) deviations from the equilibrium {state}, relating {Maxwell's displacement} in the vacuum, to the quantum properties of the vacuum.
Fine-tuning cell-mimicking polyacrylamide microgels: Sensitivity to
microscale reaction conditions in droplet microfluidics
Ruchi Goswami,
Kyoohyun Kim,
Aldo R. Boccaccini,
Jochen Guck,
Salvatore Girardo
The ability to shape polyacrylamide (PAAm) hydrogels using droplet microfluidics enables the production of microgel particles that mimic the cellular physical properties, opening new avenues in mechanobiology. Precisely controlling microgel size and elasticity is crucial yet complex, as various factors influence polymerization and the resulting network structure. While it is well established that chemical reactions in microdroplets are typically faster and more homogeneous than in bulk systems, an often-overlooked aspect is the increased sensitivity of these microreactors: minor variations in chemical or physical parameters can lead to significant changes in the resulting microgel properties. Our study identifies flow conditions as a key factor influencing both microgel elasticity and size, by modulating interfacial transport during gelation. Using a flow-focusing microfluidic chip, we generated pre-gel droplets with identical composition dispersed in an oil phase, systematically varying the PAAm-to-oil flow rate ratio while keeping the total flow rate constant. This approach yields droplets with minimal diameter variation (< 1um), yet produces beads with distinct Young's moduli, despite identical total monomer concentrations. Further analysis revealed that the catalyst transport across the oil-water interface significantly affects the polymerization efficiency and polymer network. Our findings highlight that despite the advantages of droplet-based polymerization, achieving reproducible microgel properties requires careful control of flow parameters. This underscores the importance of precise microfluidic control in advancing PAAm microgel applications in biophysics.
Brillouin-enhanced four-wave mixing with optical chiral states
Brillouin-enhanced four-wave mixing (BE-FWM)—also known as Brillouin dynamic gratings—is an important nonlinear effect in photonics that couples four light waves by traveling acoustic waves. The effect has received much attention in the past few decades, especially for applications in fiber sensing, signal processing, and optical delay lines. Here, we report BE-FWM with optical chiral states (i.e., circular polarization and vortex states) in twisted photonic crystal fiber, by leveraging the topology-selective Brillouin effect. Phase-matching has the consequence that the traveling acoustic gratings created by circularly polarized vortex pump and Stokes in the stimulated Brillouin scattering can be used to modulate a frequency-shifted probe, where the pump/Stokes and probe have different circular polarization or topological charges. Based on our findings, we demonstrate cross-frequency selective information transfer and show that the information is transferred only when pump and probe have opposite circular polarization.
Phase-adaptive cooling of fringe-trapped nanoparticles at room temperature in hollow-core photonic crystal fiber
Soumya Chakraborty,
Gordon Wong,
Pardeep Kumar,
Hyunjun Nam,
Claudiu Genes,
Nicolas Joly
Active feedback cooling of levitated dielectric particles is a pivotal technique for creating ultrasensitive sensors and probing fundamental physics. Here we demonstrate phase-adaptive feedback cooling of silica nanoparticles optically trapped in standing-wave potential formed by two co-linearly polarized counterpropagating diffraction-free guided modes in a hollow-core photonic crystal fiber at room temperature. Unlike standard laser intensity- or Coulomb force-based feedback, our approach modulates the relative optical phase between the counterpropagating fundamental modes proportionally to the particle's axial momentum. This generates a Stokes-like dissipative force which effectively damps the center-of-mass motion without introducing excess heating and can also work with uncharged particles. At 2 mbar air pressure, the axial center-of-mass temperature of a 195 nm silica particle is reduced by half upon application of the feedback and to 58.6 K at 0.5 mbar. The measured mechanical spectra agree well with our analytical model, validating the cooling mechanism. We envision this approach will open up pathways towards long-range, coherent control of mesoscopic particles inside hollow-core fibers, offering a fiber-integrated versatile platform for future quantum manipulation.
Bioengineered Bacterial Vesicles and Biomimetic Hybrids Eliminate Biofilms and Balance the Gut Microbiome
Leila Pourtalebi Jahromi,
Benedikt Kronast,
Jennifer Munkert,
Lorenzo Sana,
Marcus Koch,
Heike Danzer,
Sirka Dormeyer,
Shuhan Jiang,
Fabian Herrmann, et al.
Antibiotic-resistant pathogens are a global health challenge, necessitating innovative solutions beyond conventional antibiotics. This study introduces biomimetic nanocarriers - hybrids of bacteriomimetic liposomes and biocompatible Myxobacteria outer-membrane vesicles (OMVs) - as tunable platforms for targeted antibiotic delivery. Comparative analyses of their physicochemical properties and interactions with immune cells, intestinal epithelium, and biofilm-forming pathogens reveal distinct advantages. Hybrids excel at delivering antibiotics to intracellular targets, while Myxobacteria OMVs, particularly those of strain SBSr 073, evade immune clearance and prolong extracellular drug exposure. To support clinical translation, this study optimizes antibiotic encapsulation methods for SBSr 073 OMVs and evaluates the short- and long-term impact of Cystobacter ferrugineus 23 strain OMVs on the gut microbiome in mice. Summing up, this study highlights the promise of Myxobacteria OMVs and their biomimetic hybrids as versatile tools for treating Gram-negative biofilm-forming pathogens. These findings underscore the potential of bioengineered and biomimetic drug carriers for combating antimicrobial resistance and pave the way for their translation toward difficult-to-treat infections.
Cavity-less Brillouin strong coupling in a solid-state continuous system
Laura Blázquez Martínez,
Changlong Zhu,
Birgit Stiller
Strongly coupling two systems allows them to exchange coherent information before the systems decohere. This important regime in light-matter interactions has predominantly been reached in optical resonator configurations. In this work, we present the experimental realization of strong coupling between optical and acoustic fields within a continuum of modes in a cavity-less configuration after a single-pass through an optical waveguide. The underlying physical effect of anti-Stokes Brillouin-Mandelstam scattering in a highly nonlinear fiber at T = 4 K allows us to experimentally demonstrate strong coupling in a waveguide scenario. We show the splitting of the optoacoustic spectral response and introduce a novel technique to measure the avoided crossing of hybrid optoacoustic modes via forced detuning. This demonstration opens a path towards in-line acoustic-waves-based quantum signal processing in waveguide systems.
Quantum equilibrium propagation for efficient training of quantum systems based on Onsager reciprocity
Clara C. Wanjura,
Florian Marquardt
Nature Communications
16
6595
(2025)
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The widespread adoption of machine learning and artificial intelligence in all branches of science and technology creates a need for energy-efficient, alternative hardware. While such neuromorphic systems have been demonstrated in a wide range of platforms, it remains an open challenge to find efficient and general physics-based training approaches. Equilibrium propagation (EP), the most widely studied approach, has been introduced for classical energy-based models relaxing to an equilibrium. Here, we show a direct connection between EP and Onsager reciprocity and exploit this to derive a quantum version of EP. For an arbitrary quantum system, this can now be used to extract training gradients with respect to all tuneable parameters via a single linear response experiment. We illustrate this new concept in examples in which the input or the task is of quantum-mechanical nature, e.g., the recognition of many-body ground states, phase discovery, sensing, and phase boundary exploration. Quantum EP may be used to solve challenges such as quantum phase discovery for Hamiltonians which are classically hard to simulate or even partially unknown. Our scheme is relevant for a variety of quantum simulation platforms such as ion chains, superconducting circuits, Rydberg atom tweezer arrays and ultracold atoms in optical lattices.
Controlling treatment toxicity in ovarian cancer to prime the patient for tumor extinction therapy
Kit Gallagher,
Rachel S. Sousa,
Chandler Gatenbee,
Ryan Schenck,
Peng Chen,
Timon Citak,
Sydney Leither,
Lucia Mazzacurati,
Agata Xella, et al.
bioRxiv 10.1101/2025.07.10.664235
(2025)
| Preprint
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High-grade serous ovarian cancer (HGSOC) remains a major clinical challenge. In particular among those patients with homologous recombination (HR)-proficient tumors (>50%), most eventually succumb to their disease due to high recurrence rates, acquired resistance, and cumulative toxicity. This report summarizes work from the 12th IMO Workshop in which we explored an alternative “extinction therapy” strategy for frontline treatment of HGSOC. Inspired by ecological principles, this multi-strike approach aims to eradicate tumors not through a singular “magic bullet” but through a series of therapies after standard frontline treatment when the tumor is still, and perhaps most, vulnerable. We present a framework leveraging mathematical modeling (MM) to develop personalized multi-strike protocols for HGSOC. Key contributions include: 1) An “IMOme” score using liquid biopsy data to assess patient-specific hematopoietic toxicity risk, guiding the timing and selection of subsequent therapies, 2) MM strategies to design effective lowdose combinations of targeted agents to achieve synthetic lethality while managing toxicity, and 3) A MM framework to analyze the interplay between chemotherapy, gut microbiome toxicity, and immunotherapy, demonstrating how mitigating microbiome damage could enhance immune response. Overall, the computational approaches presented herein aim to support the design of personalized, multi-strike regimens in the frontline setting that proactively target tumor extinction while managing toxicity, ultimately seeking to deliver cures for patients with HGSOC.
Nonequilibrium Structure and Relaxation in Active Microemulsions
Rakesh Chatterjee,
Hui-Shun Kuan,
Frank Jülicher,
Vasily Zaburdaev
Microphase separation is common in active biological systems as exemplified by the separation of RNA- and DNA-rich phases in the cell nucleus driven by the transcriptional activity of polymerase enzymes acting similarly to amphiphiles in a microemulsion. Here we propose an analytically tractable model of an active microemulsion to investigate how the activity affects its structure and relaxation dynamics. Continuum theory derived from a lattice model exhibits two distinct regimes of the relaxation dynamics and is linked to the broken detailed balance due to intermittent activity of the amphiphiles.
Waveguide quantum electrodynamics (QED) provides a powerful framework for engineering quantum interactions, traditionally relying on periodic photonic arrays with continuous energy bands. Here, we investigate waveguide QED in a fundamentally different environment: A one-dimensional photonic array whose hopping strengths are structured aperiodically according to the deterministic Fibonacci-Lucas substitution rule. These "Fibonacci waveguides" lack translational invariance and are characterized by a singular continuous energy spectrum and critical eigenstates, representing a deterministic intermediate between ordered and disordered systems. We demonstrate how to achieve decoherence-free, coherent interactions in this unique setting. We analyze two paradigmatic cases: (i) Giant emitters resonantly coupled to the simplest aperiodic version of a standard waveguide. For these, we show that atom photon bound states form only for specific coupling configurations dictated by the aperiodic sequence, leading to an effective atomic Hamiltonian, which itself inherits the Fibonacci structure; and (ii) emitters locally and off-resonantly coupled to the aperiodic version of the Su-Schrieffer-Heeger waveguide. In this case the mediating bound states feature aperiodically modulated profiles, resulting in an effective Hamiltonian with multifractal properties. Our work establishes Fibonacci waveguides as a versatile platform, which is experimentally feasible, demonstrating that the deterministic complexity of aperiodic structures can be directly engineered into the interactions between quantum emitters.
Consensus statement on Brillouin light scattering microscopy of biological materials
Pierre Bouvet,
Carlo Bevilacqua,
Yogeshwari Ambekar,
Giuseppe Antonacci,
Joshua Au,
Silvia Caponi,
Sophie Chagnon-Lessard,
Juergen Czarske,
Thomas Dehoux, et al.
Nature Photonics
19
681-691
(2025)
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Brillouin light scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the submicrometre scale. Over the past decade, BLS has found increasing microscopy applications in the life sciences, driven by the observed importance of mechanical properties in biological processes, the realization of more sensitive BLS spectrometers and the extension of BLS to an imaging modality. As with other spectroscopic techniques, BLS measurements detect not only signals that are characteristic of the investigated sample, but also those of the experimental apparatus, and can be substantially affected by measurement conditions. Here we report a consensus between researchers in the field. We aim to improve the comparability of BLS studies by providing reporting recommendations for the measured parameters and detailing common artefacts. Given that most BLS studies of biological matter are still at proof-of-concept stages and use different, often self-built, spectrometers, a consensus statement is particularly timely to ensure unified advancement.
Reinforcement Failing guides the discovery of emergent spatial dynamics in adaptive tumor therapy
Serhii Aif,
Maximilian Eiche,
Nico Appold,
Elias Fischer,
Timon Citak,
Jona Kayser
bioRxiv 10.1101/2025.04.08.647768
(2025)
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Artificial intelligence is revolutionizing oncology by transforming how malignancies are diagnosed, cancer biology is understood, and therapeutics are discovered. A cornerstone of this progress has been the availability of extensive, carefully curated datasets. Similarly, advances in AI-guided therapeutic strategies via Reinforcement Learning (RL) hinge upon carefully designed computational training environments that are both efficient and sufficiently realistic to capture key dynamics of cancer growth and therapy response. However, designing suitable models remains challenging for solid tumors, where emergent physical phenomena significantly influence therapeutic outcomes. Here, we introduce Reinforcement Failing, an AI-guided scientific discovery framework designed to reveal emergent physical mechanisms in tumor therapy. Applying this approach to adaptive therapy in solid tumors, we identify the pivotal roles of position-dependent proliferation and mechanically driven collective motion of resistant cells. Our findings highlight how integrating tumor physics into therapeutic strategies can optimize outcomes while mitigating hidden pitfalls during translation. Together, these results demonstrate that Reinforcement Failing is a powerful artificial scientific discovery engine for deciphering high-complexity processes in personalized cancer treatment and beyond.
Frequency conversion of vortex states by chiral forward Brillouin scattering in twisted photonic crystal fibre
Xinglin Zeng,
Philip St.J. Russell,
Birgit Stiller
Photonics Research
13
1997-2012
(2025)
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Optical vortex states-higher optical modes with helical phase progression and carrying orbital angular momentum-have been explored to increase the flexibility and capacity of optical fibres employed for example in mode-division-multiplexing, optical trapping and multimode imaging. A common requirement in such systems is high fidelity transfer of signals between different frequency bands and modes, which for vortex modes is not so straightforward. Here we report intervortex conversion between backward-propagating circularly polarised vortex modes at one wavelength, using chiral flexural phonons excited by chiral forward stimulated Brillouin scattering at a different wavelength. The experiment is carried out using chiral photonic crystal fibre, which robustly preserves circular polarisation states. The chiral acoustic wave, which has the geometry of a spinning single-spiral corkscrew, provides the orbital angular momentum necessary to conserve angular momentum between the coupled optical vortex modes. The results open up new opportunities for interband optical frequency conversion and the manipulation of vortex states in both classical and quantum regimes.
A neural-network-based Python package for performing large-scale atomic CI using pCI and other high-performance atomic codes
Pavlo Bilous,
Charles Cheung,
Marianna Safronova
Computer Physics Communications
315
109731
(2025)
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Modern atomic physics applications in science and technology pose ever higher demands on the precision of compu- tations of properties of atoms and ions. Especially challenging is the modeling of electronic correlations within the configuration interaction (CI) framework, which often requires expansions of the atomic state in huge bases of Slater determinants or configuration state functions. This can easily render the problem intractable even for highly efficient atomic codes running on distributed supercomputer systems. Recently, we have successfully addressed this problem using a neural-network (NN) approach [1]. In this work, we present our Python code for performing NN-supported large-scale atomic CI using pCI [2] and other high-performance atomic codes.
Small U-Net for Fast and Reliable Segmentation in Imaging Flow Cytometry
Sara Kaliman,
Raghava Alajangi,
Nadia Sbaa,
Paul Müller,
Nadine Ströhlein,
Jeffrey Harmon,
Martin Kräter,
Jochen Guck,
Shada Abuhattum Hofemeier
Cytometry Part A
107
(7)
450-463
(2025)
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Imaging flow cytometry requires rapid and accurate segmentation methods to ensure high-quality cellular morphology analysis and cell counting. In deformability cytometry (DC), a specific type of imaging flow cytometry, accurately detecting cell contours is critical for evaluating mechanical properties that serve as disease markers. Traditional thresholding methods, commonly used for their speed in high-throughput applications, often struggle with low-contrast images, leading to inaccuracies in detecting the object contour. Conversely, standard neural network approaches like U-Net, though effective in medical imaging, are less suitable for high-speed imaging applications due to long inference times. To address these issues, we present a solution that enables both fast and accurate segmentation, designed for imaging flow cytometry. Our method employs a small U-Net model trained on high-quality, curated, and annotated data. This optimized model outperforms traditional thresholding methods and other neural networks, delivering a 35× speed improvement on CPU over the standard U-Net. The enhanced performance is demonstrated by a significant reduction in systematic measurement errors in blood samples analyzed using DC. The tools developed in this study are adaptable for various imaging flow cytometry applications. This approach improves segmentation quality while maintaining the rapid processing necessary for high-throughput environments.
Generation of photon pairs through spontaneous four-wave mixing in subwavelength nonlinear films
Changjin Son,
Samuel Peana,
Owen Matthiessen,
Artem Kryvobok,
Alexander Senichev,
Alexandra Boltasseva,
Vladimir M. Shalaev,
Maria Chekhova
Pairs of entangled photons are crucial for photonic quantum technologies. The demand for integrability and multi-functionality suggests flat platforms—ultrathin layers and metasurfaces—as sources of photon pairs. Despite the success in the demonstration of spontaneous parametric downconversion (SPDC) from such sources, there are almost no works on spontaneous four-wave mixing (SFWM)—an alternative process to generate photon pairs. Meanwhile, SFWM can be implemented in any nanostructures, including ones made of isotropic materials, which are easier to fabricate than crystalline SPDC sources. Here, we investigate photon pair generation through SFWM in subwavelength films of amorphous silicon nitride (SiN) with varying nitrogen content. For all samples, we demonstrate two-photon quantum correlations, indicated by the normalized second-order correlation function g(2)(0): it exceeds 2 and decays as the pump power increases. By observing two-photon interference between SFWM from the SiN films and the fused silica (FS) substrate, we find the third-order susceptibilities of films with different nitrogen content.
Editors' Pick
Monolithic electric field control of a grating coupler for finely tuning wavelength, efficiency, and bandwidth
Yifan Zhang,
Yongyong Zhuang,
Liu Yang,
Xin Liu,
Qingyuan Hu,
Haochen Yan,
Hao Zhang,
Yaojing Zhang,
Shuangyou Zhang, et al.
In this Letter, we propose a Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3-On-Insulator (PIN-PMN-PTOI) based apodized grating coupler through finite-difference time-domain (FDTD) simulations. By leveraging the ultrahigh electro-optic coefficient of PIN-PMN-PT single crystal, we demonstrate precise control over the effective refractive index, thereby fine-tuning the central wavelength, enhancing coupling efficiency (CE) and 3-dB bandwidth. Simulation results reveal that, under an external electric field, the center wavelength can be tuned from 1544.84 nm to 1553.36 nm, while the CE remains above 80%. The CE can be improved by 5.9%; the 3-dB bandwidth can be increased by 2.1 nm at 1550 nm. Our results show that PIN-PMN-PTOI-based gratings are promising for large-scale photonic chip integration with high CE and large bandwidth.
The non-Hermitian skin effect: A perspective
Julius Gohsrich,
Ayan Banerjee,
Flore K. Kunst
Europhysics Letters
150
60001
(2025)
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The non-Hermitian (NH) skin effect is a truly NH feature, which manifests itself as an accumulation of states, known as skin states, on the boundaries of a system. In this perspective, we discuss several aspects of the NH skin effect, focusing on the most interesting facets of this phenomenon. Beyond reviewing necessary requirements to see the NH skin effect, we discuss the NH skin effect as a topological effect that can be seen as a manifestation of a truly NH bulk-boundary correspondence, stemming from the spectral topology, and show how skin states can be distinguished from topological boundary states. As most theoretical work has focused on studying the NH skin effect in one-dimensional non-interacting systems, recent developments of studying this effect in higher dimensions as well as in many-body systems are highlighted. Lastly, experimental signatures and applications are discussed, and an outlook is provided.
Towards satellite tests combining general relativity and quantum mechanics through quantum optical interferometry: progress on the deep space quantum link
Makan Mohageg,
Charis Anastopoulos,
Olivia Brasher,
Jason Gallicchio,
Bei Lok Hu,
Thomas Jennewein,
Spencer Johnson,
Shih-Yuin Lin,
Alexander Ling, et al.
EPJ Quantum Technology
12
78
(2025)
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The Deep Space Quantum Link (DSQL) is a space-mission concept that aims to explore the interplay between general relativity and quantum mechanics using quantum optical interferometry. This mission concept was formally presented to the United States National Academy of Science Decadal Survey as a research campaign for Fundamental Physics in 2022. Since then, advances have been made in the space-based quantum optical technologies required to conduct a DSQL-type mission. In addition, other research efforts have defined alternative measurement concepts to explore the same scientific questions motivating the DSQL mission. This paper serves as an update to the community on the status of the DSQL mission concept and related research and technology development efforts.
Nonlinear Metafiber: On-fiber 3D Nanoprinted Metalenses to Enhance Ultrafast Supercontinuum Generation in Suspended Core Fibers
Shahrzad Hosseinabadi,
Johannes Hofmann,
Torsten Wieduwilt,
Xue Qi,
Michael Frosz,
Markus A. Schmidt
Supercontinuum generation (SCG) using ultrashort pulses is a highly efficient technique for achieving broad nonlinear frequency conversion, with suspended core fibers (SCFs) being particularly effective due to their high modal field concentration and precise dispersion control. However, their small core sizes, typically a few micrometers, pose significant challenges for light incoupling, resulting in a low and unstable coupling that often requires complex high numerical aperture bulk optics that are both costly and difficult to integrate. This work addresses this key challenge by introducing the concept of nonlinear metafibers. By implementing tailored metalenses directly on the end faces of SCFs using advanced 3D nanoprinting, we demonstrate alignment-free and highly robust coupling of broadband ultrashort pulses into small-core SCFs. This first demonstration of a nonlinear metafiber achieves full all-fiber integration, eliminating the need for bulky external optical components and facilitating broadband soliton-based SCG. The flexibility of this novel approach, which effectively overcomes a fundamental problem in nonlinear photonics, has broad applicability in various fields including quantum technology and life sciences. In addition, the concept extends beyond SCFs to other fiber types and on-chip waveguides, paving the way for new opportunities in nonlinear photonics and integrated optics. This study establishes nonlinear metafibers as a transformative platform with the potential to advance applications in which efficient, compact, and robust nonlinear photonic systems are critical.
Many-Body Neural Network Wavefunction for a Non-Hermitian Ising Chain
Lavoisier Wah,
Remmy Augusta Menzata Zen,
Flore K. Kunst
Non-Hermitian (NH) quantum systems have emerged as a powerful framework for describing open quantum systems, non-equilibrium dynamics, and engineered quantum optical materials. However, solving the ground-state properties of NH systems is challenging due to the exponential scaling of the Hilbert space, and exotic phenomena such as the emergence of exceptional points. Another challenge arises from the limitations of traditional methods like exact diagonalization (ED). For the past decade, neural networks (NN) have shown promise in approximating many-body wavefunctions, yet their application to NH systems remains largely unexplored. In this paper, we explore different NN architectures to investigate the ground-state properties of a parity-time-symmetric, one-dimensional NH, transverse field Ising model with a complex spectrum by employing a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), and a multilayer perceptron (MLP). We construct the NN-based many-body wavefunctions and validate our approach by recovering the ground-state properties of the model for small system sizes, finding excellent agreement with ED. Furthermore, for larger system sizes, we demonstrate that the RNN outperforms both the RBM and MLP. However, we show that the accuracy of the RBM and MLP can be significantly improved through transfer learning, allowing them to perform comparably to the RNN for larger system sizes. These results highlight the potential of neural network-based approaches--particularly for accurately capturing the low-energy physics of NH quantum systems.
Frequency conversion in a hydrogen-filled hollow-core fiber: power scaling, background, and bandwidth
Anica Hamer,
Frank Vewinger,
Michael Frosz,
Simon Stellmer
Large-area quantum networks based on optical fibers allow photons at near-infrared wavelengths to travel with minimal loss. Quantum frequency conversion is a method to alter the wavelength of a single photon while maintaining its quantum state. Most commonly, nonlinear crystals are employed for this conversion process, where near-unity conversion efficiency at high fidelity has been demonstrated. Still, the crystal-based conversion process is plagued by strong background noise, very limited spectral bandwidth, and inhomogeneous temperature profiles at strong pump fields. In the previous work, we have demonstrated frequency conversion in hydrogen-filled hollow-core fibers and claimed that this conversion process does not compromise performance at strong pump fields, is essentially free of background noise, and is intrinsically broadband. Here, we demonstrate that these three claims are justified: we demonstrate the quadratic scaling with pump field intensity, quantify the background level, and present coarse tuning over a range of 10 nm.
Cell state-specific cytoplasmic density controls spindle architecture and scaling
Nature Cell Biology
27
959-971
(2025)
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Mitotic spindles are dynamically intertwined with the cytoplasm they assemble in. How the physicochemical properties of the cytoplasm affect spindle architecture and size remains largely unknown. Using quantitative biochemistry in combination with adaptive feedback microscopy, we investigated mitotic cell and spindle morphology during neural differentiation of embryonic stem cells. While tubulin biochemistry and microtubule dynamics remained unchanged, spindles changed their scaling behaviour; in differentiating cells, spindles were considerably smaller than those in equally sized undifferentiated stem cells. Integrating quantitative phase imaging, biophysical perturbations and theory, we found that as cells differentiated, their cytoplasm became more dilute. The concomitant decrease in free tubulin activated CPAP (centrosomal P4.1-associated protein) to enhance the centrosomal nucleation capacity. As a consequence, in differentiating cells, microtubule mass shifted towards spindle poles at the expense of the spindle bulk, explaining the differentiation-associated switch in spindle architecture. This study shows that cell state-specific cytoplasmic density tunes mitotic spindle architecture. Thus, we reveal physical properties of the cytoplasm as a major determinant in organelle size control.
Field-resolved attosecond solitons
Amelie M. Heinzerling,
Francesco Tani,
Manoram Agarwal,
Vladislav S. Yakovlev,
Ferenc Krausz,
Nicholas Karpowicz
Nature Photonics
19
772-777
(2025)
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Here we harness soliton dynamics in a hollow-core fibre to generate attosecond laser pulses spanning the deep ultraviolet (DUV) to the near infrared, and we record their electric-field waveforms using nonlinear photoconductive sampling. By combining these techniques, we measure ultrashort pulses containing a soliton at optical wavelengths and generated a resonant dispersive wave covering the DUV regime with a total pulse duration of 350 attoseconds full width at half maximum of the squared field, demonstrating the extension of the electric-field sampling bandwidth to ultrashort wavelengths. Therefore, we provide a flexible and efficient route to the generation of intense isolated attosecond pulses complementary to those based on high-harmonic generation in gases, in a spectral range particularly interesting for studies in solids and in molecules. Finally, we show that these subcycle DUV–near-infrared pulses provide sufficient intensities to ionize argon and, thus, access attosecond strong-field laser physics in these spectral regions.
A compact analytical solution of the Dicke superradiance master equation via residue calculus
Raphael Holzinger,
Claudiu Genes
Zeitschrift für Naturforschung, A: Physical Sciences
80
673-679
(2025)
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We revisit the Dicke superradiance problem, where an ensemble of N identical two-level systems undergoes collective spontaneous decay. While an exact analytical solution has been known since 1977, its algebraic complexity has hindered practical use. Here we present a compact, closed-form solution that expresses the dynamics as a finite sum over residues or, equivalently, a complex contour integral. The method yields explicit populations of all Dicke states at arbitrary times and system sizes, and generalizes naturally to arbitrary initial conditions. Our formulation is computationally efficient and offers structural insights into the role of spectral degeneracies and Lindbladian eigenmodes in collective decay.
Velocity-modulated drag-trapping of nanoparticles by moving fringe pattern in hollow-core fiber
Soumya Chakraborty,
Gordon Wong,
Philip Russell,
Nicolas Joly
We report optical trapping and transport at atmospheric pressure of nanoparticles in a moving interference pattern in hollow-core photonic crystal fiber. Unlike in previous work at low pressure, when the viscous drag forces are weak and the particles travel at the fringe velocity, competition between trapping and drag forces causes the particle velocity to oscillate as it is momentarily captured and accelerated by each passing fringe, followed by release and deceleration by viscous forces. As a result the average particle velocity is lower than the fringe velocity. An analytical model of the resulting motion shows excellent agreement with experiment. We predict that nanoparticles can be trapped at field nodes if the fringes are rocked to and fro sinusoidally-potentially useful for reducing the exposure of sensitive particles to trapping radiation. The high precision of this new technique makes it of interest for example in characterizing nanoparticles, exploring viscous drag forces in different gases and liquids, and temperature sensing.
Brillouin-based storage of QPSK signals with fully tunable phase retrieval
Olivia Saffer,
Jesús Humberto Marines Cabello,
Steven Becker,
Andreas Geilen,
Birgit Stiller
Photonic memory is an important building block to delay, route and buffer optical information, for instance in optical interconnects or for recurrent optical signal processing. Photonic-phononic memory based on stimulated Brillouin-Mandelstam scattering (SBS) has been demonstrated as a coherent optical storage approach with broad bandwidth, frequency selectivity and intrinsic nonreciprocity. Here, we experimentally demonstrated the storage of quadrature-phase encoded data at room temperature and at cryogenic temperatures. We store and retrieve the 2-bit states {00,01,10,11} encoded as optical pulses with the phases {0,pi/2,pi,3pi/2} - a quadrature phase shift keying (QPSK) signal. The 2-bit signals are retrieved from the acoustic domain with a global phase rotation of π, which is inherent in the process due to SBS. We also demonstrate full phase control over the retrieved data based on two different handles: by detuning slightly from the SBS resonance, or by changing the storage time in the memory scheme we can cover the full range [0,2pi). At a cryogenic temperature of 3.9 K, we have increased readout efficiency as well as gained access to longer storage times, which results in a detectable signal at 140 ns. All in all, the work sets the cornerstone for optoacoustic memory schemes with phase-encoded data
Cellular morphodynamics as quantifiers for functional states of resident tissue macrophages in vivo
Miriam Schnitzerlein,
Eric Greto,
Anja Wegner,
Anna Möller,
Oliver Aust,
Oumaima Ben Brahim,
David B. Blumenthal,
Vasily Zaburdaev,
Stefan Uderhardt, et al.
PLOS Computational Biology
21
e1011859
(2025)
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Resident tissue macrophages (RTMs) are essential for tissue homeostasis. Their diverse functions, from monitoring interstitial fluids to clearing cellular debris, are accompanied by characteristic morphological changes that reflect their functional status. While current knowledge of macrophage behaviour comes primarily from in vitro studies, their dynamic behavior in vivo is fundamentally different, necessitating a more physiologically relevant approach to their understanding. In this study, we employed intravital imaging to generate dynamic data from peritoneal RTMs in mice under various conditions and developed a comprehensive image processing pipeline to quantify RTM morphodynamics over time, defining human-interpretable cell size and shape features. These features allowed for the quantitative and qualitative differentiation of cell populations in various functional states, including pro- and anti-inflammatory activation and endosomal dysfunction. The study revealed that under steady-state conditions, RTMs exhibit a wide range of morphodynamical phenotypes, constituting a naïve morphospace of behavioral motifs. Upon challenge, morphodynamic patterns changed uniformly at the population level but predominantly within the constraints of this naïve morphospace. Notably, aged animals displayed a markedly shifted naïve morphospace, indicating drastically different behavioral patterns compared to their young counterparts. The developed method also proved valuable in optimizing explanted tissue setups, bringing RTM behavior closer to the physiological native state. Our versatile approach thus provides novel insights into the dynamic behavior of bona fide macrophages in vivo, enabling the distinction between physiological and pathological cell states and the assessment of functional tissue age on a population level.
Quantum computing and artificial intelligence: status and perspectives
Giovanni Acampora,
Andris Ambainis,
Natalia Ares,
Leonardo Banchi,
Pallavi Bhardwaj,
Daniele Binosi,
G. Andrew D. Briggs,
Tommaso Calarco,
Vedran Dunjko, et al.
This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could support the development of innovative AI solutions. It also examines use cases of classical AI that can empower research and development in quantum technologies, with a focus on quantum computing and quantum sensing. The purpose of this white paper is to provide a long-term research agenda aimed at addressing foundational questions about how AI and quantum computing interact and benefit one another. It concludes with a set of recommendations and challenges, including how to orchestrate the proposed theoretical work, align quantum AI developments with quantum hardware roadmaps, estimate both classical and quantum resources - especially with the goal of mitigating and optimizing energy consumption - advance this emerging hybrid software engineering discipline, and enhance European industrial competitiveness while considering societal implications.
Prospects of phase-adaptive cooling of levitated magnetic particles in a hollow-core photonic-crystal fibre
P. Kumar,
F. G. Jimenez,
S. Chakraborty,
G. K. L. Wong,
N. Y. Joly,
C. Genes
Physical Review Research
7
023191
(2025)
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We analyze the feasibility of cooling of classical motion of a micro- to nano-sized magnetic particle, levitated inside a hollow-core photonic crystal fiber. The cooling action is implemented by means of controlling the phase of one of the counter-propagating fiber guided waves. Direct imaging of the particle's position, followed by the subsequent updating of the control laser's phase leads to Stokes type of cooling force. We provide estimates of cooling efficiency and final achievable temperature, taking into account thermal and detection noise sources. Our results bring forward an important step towards using trapped micro-magnets in sensing, testing the fundamental physics and preparing the quantum states of magnetization.
Tutorial: Hong-Ou-Mandel interference with Structured Photons
Tareq Jaouni,
Xuemei Gu,
Mario Krenn,
Alessio D'Errico,
Ebrahim Karimi
The Hong-Ou-Mandel (HOM) effect, an effective two-photon interference phenomenon, is a cornerstone of quantum optics and a key tool for lin- ear optical quantum information processing. While the HOM effect has been extensively studied both theoretically and experimentally for various photonic quantum states, particularly in the spectral domain, detailed overviews of its behaviour for structured photons – those with complex spatial profiles – under arbitrary spatial mode measurement schemes are still lacking. This tutorial aims to fill this gap by providing a comprehensive theoretical analysis of the HOM effect for structured photons, including an arbitrary mode projection on quantum interference outcomes. The tutorial also provides analytical, closed-form expressions of the HOM visibility under different measurement conditions, which is a crucial contribution for its application in computational and artificial-intelligence-driven discovery of new quantum experiments exploiting the power of photons with complex spatial modes.
Microresonator soliton frequency combs via cascaded Brillouin scattering
Hao Zhang,
Shuangyou Zhang,
Toby Bi,
George N. Ghalanos,
Yaojing Zhang,
Haochen Yan,
Arghadeep Pal,
Jijun He,
Shilong Pan, et al.
Communications Physics
8
216
(2025)
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Microresonator frequency combs are vital for advancing optical communications and sensing, but current methods face challenges in achieving low phase noise and flexible repetition rates simultaneously. Here, we demonstrate forward-propagating soliton frequency combs using cascaded Brillouin scattering in a silica resonator. This method bridges distinct resonator modes and decouples soliton repetition rates from the Brillouin frequency shift (~10 GHz in silica). By generating soliton pulses at 107 GHz, we show that the repetition rates can be tailored through resonator geometry without compromising low noise. This integration of Brillouin lasing with microcombs unites stability and design flexibility, overcoming prior limitations. The results can enable scalable photonic platforms for applications such as LiDAR, high-capacity optical networks, and precision microwave generation. This technique is of interest for technologies that demand both ultra-stable and customizable light sources.
Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning
Maximilian Nägele,
Jan Olle,
Thomas Fösel,
Remmy Zen,
Florian Marquardt
Markov decision processes (MDPs) are used to model a wide variety of applications ranging from game playing over robotics to finance. Their optimal policy typically maximizes the expected sum of rewards given at each step of the decision process. However, a large class of problems does not fit straightforwardly into this framework: Non-cumulative Markov decision processes (NCMDPs), where instead of the expected sum of rewards, the expected value of an arbitrary function of the rewards is maximized. Example functions include the maximum of the rewards or their mean divided by their standard deviation. In this work, we introduce a general mapping of NCMDPs to standard MDPs. This allows all techniques developed to find optimal policies for MDPs, such as reinforcement learning or dynamic programming, to be directly applied to the larger class of NCMDPs. Focusing on reinforcement learning, we show applications in a diverse set of tasks, including classical control, portfolio optimization in finance, and discrete optimization problems. Given our approach, we can improve both final performance and training time compared to relying on standard MDPs.
Non-destructive real-time characterization of anti-resonant hollow-core fibers using Fabry-Pérot interferometry
Michael Frosz,
Michael Bergler,
Patrick Uebel
Optics Express
33
22961-22973
(2025)
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Reliable industrial manufacturing of anti-resonant hollow-core fibers (AR-HCFs) requires non-destructive, in-line real-time measurements of the fiber structure during drawing. Such a method was recently developed, but it suffered from measurement deviations as the fiber rotated, as well as other disadvantages. Here we demonstrate a greatly improved measurement principle based on Fabry-Pérot interference, which allows for direct measurement of the wall thickness of the AR cladding elements, the gap between capillaries, jacket thickness, and jacket inner diameter. The core diameter can also be inferred from these measurements. The method is therefore more robust, provides more useful information, and enables a significant improvement in the uniformity and length of AR-HCFs.
Forecasting high-impact research topics via machine learning on evolving knowledge graphs
Xuemei Gu,
Mario Krenn
Machine Learning: Science and Technology
6
025041
(2025)
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The exponential growth in scientific publications poses a severe challenge for human researchers. It forces attention to more narrow sub-fields, which makes it challenging to discover new impactful research ideas and collaborations outside one’s own field. While there are ways to predict a scientific paper’s future citation counts, they need the research to be finished and the paper written, usually assessing impact long after the idea was conceived. Here we show how to predict the impact of onsets of ideas that have never been published by researchers. For that, we developed a large evolving knowledge graph built from more than 21 million scientific papers. It combines a semantic network created from the content of the papers and an impact network created from the historic citations of papers. Using machine learning, we can predict the dynamic of the evolving network into the future with high accuracy, and thereby the impact of new research directions. We envision that the ability to predict the impact of new ideas will be a crucial component of future artificial muses that can inspire new impactful and interesting scientific ideas.
Inverse-Designed Silicon Nitride Nanophotonics
Toby Bi,
Shuangyou Zhang,
Egemen Bostan,
Danxian Liu,
Aditya Paul,
Olga Ohletz,
Irina Harder,
Yaojing Zhang,
Alekhya Ghosh, et al.
Silicon nitride photonics has enabled integration of a variety of components for applications in linear and nonlinear optics, including telecommunications, optical clocks, astrocombs, bio-sensing, and LiDAR. With the advent of inverse design – where desired device performance is specified and closely achieved through iterative, gradient-based optimization – and the increasing availability of silicon nitride photonics via foundries, it is now feasible to expand the photonic design library beyond the limits of traditional approaches and unlock new functionalities. In this work, we present inverse-designed photonics on a silicon nitride platform and demonstrate both the design capabilities and experimental verification by realising precisely tailored wavelength-division multiplexers, mode-division multiplexers, and high-Q resonators with controllable wavelength range and dispersion. This demonstrates inverse-designed enhanced manipulation of orthogonal bases of light. Furthermore, we use these inverse-designed structures to form optical cavities that hold promise for on-chip nonlinear and quantum optics experiments.
Indistinguishable MHz-narrow heralded photon pairs from a whispering gallery resonator
Sheng-Hsuan Huang,
Thomas Dirmeier,
Golnoush Shafiee,
Kaisa Laiho,
Dmitry Strekalov,
Andrea Aiello,
Gerd Leuchs,
Christoph Marquardt
The Hong–Ou–Mandel interference plays a vital role in many quantum optical applications where indistinguishability of two photons is important. Such photon pairs are commonly generated as the signal and idler in the polarization-degenerate spontaneous parametric downconversion (SPDC). To scale this approach to a larger number of photons, we demonstrate how two independent signal photons radiated into different spatial modes can be rendered conditionally indistinguishable by a heralding measurement performed on their respective idlers. We use the SPDC in a whispering gallery resonator, which is already proven to be versatile sources of quantum states. Its extreme conversion efficiency allowed us to perform our measurements with only 50 nW of in-coupled pump power in each propagation direction. The Hong–Ou–Mandel interference of two counterpropagating signal photons manifested itself in the fourfold coincidence rate, where the detection of two idler photons heralds a pair of signal photons with a desired temporal overlap. We achieved the Hong–Ou–Mandel dip contrast of 74% ± 5%. Importantly, the optical bandwidth of all involved photons is of the order of a MHz and is continuously tunable. This, on the one hand, makes it possible to achieve the necessary temporal measurement resolution with standard electronics and, on the other hand, creates a quantum state source compatible with other candidates for qubit implementation, such as optical transitions in solid-state or vaporous systems. We also discuss the possibility of generating photon pairs with similar temporal modes from two different whispering gallery resonators.
Hierarchical Heterogeneities in Spatio-Temporal Dynamics of the Cytoplasm
Understanding of the dynamics inherent to biological matter is crucial for illuminating the physical mechanisms underlying cellular processes. In this study, we employ bright-field differential dynamic microscopy (DDM) to investigate density fluctuations inherent in a cell-free model of eukaryotic cytoplasm. Our measurements reveal subdiffusive fractional Brownian motion and non-Gaussian displacement distributions, highlighting cytoplasmic heterogeneity. We introduce an empirical model that combines fractional Brownian motion with an inverse Gaussian distribution of diffusivities to describe the observed non-Gaussianity. Validated through Monte Carlo simulations, this model allows us to estimate the fractional diffusivity and exponent effectively. By altering macromolecular composition, the addition of energy, and assembly of a cytoskeleton, we identify three independent mechanisms that result in similar fractional exponents yet distinct diffusivities. We find that energy addition leads to non-stationary dynamics, in contrast to the stationary behavior observed under passive conditions. Presence of microtubules introduces a secondary dynamical timescale, which we describe using a two-state fractional Brownian motion model to differentiate between cytosolic and microtubule network associated contributions. Our findings demonstrate the effectiveness of DDM as a label-free tool for quantifying viscoelastic and heterogeneous properties of the cytoplasm and provide insights into how physical and biochemical factors, including cytoskeletal organization, govern subcellular dynamics.
Lie symmetries and ghost-free representations of the Pais-Uhlenbeck model
We investigate the Pais-Uhlenbeck (PU) model, a paradigmatic example of a higher time-derivative theory, by identifying the Lie symmetries of its associated fourth- order dynamical equation. Exploiting these symmetries in conjunction with the model’s Bi-Hamiltonian structure, we construct distinct Poisson bracket formulations that pre- serve the system’s dynamics. Amongst other possibilities, this allow us to recast the PU model in a positive definite manner, offering a solution to the long-standing problem of ghost instabilities. Furthermore, we systematically explore a family of transformations that reduce the PU model to equivalent first-order, higher-dimensional systems. Finally we examine the impact on those transformations by adding interaction terms of poten- tial form to the PU model and demonstrate how they usually break the Bi-Hamiltonian structure. Our approach yields a unified framework for interpreting and stabilizing higher time-derivative dynamics through a symmetry analysis in some parameter regime.
Differential tissue deformability underlies fluid pressure-driven shape divergence of the avian embryonic brain and spinal cord
Susannah B.P. McLaren,
Shi-Lei Xue,
Siyuan Ding,
Alexander K. Winkel,
Oscar Baldwin,
Shreya Dwarakacherla,
Kristian Franze,
Edouard Hannezo,
Fengzhu Xiong
Developmental Cell
60
2237-2247.E4
(2025)
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An enlarged brain underlies the complex central nervous system of vertebrates. The dramatic expansion of the brain that diverges its shape from the spinal cord follows neural tube closure during embryonic development. Here, we show that this differential deformation is encoded by a pre-pattern of tissue material properties in chicken embryos. Using magnetic droplets and atomic force microscopy, we demonstrate that the dorsal hindbrain is more fluid than the dorsal spinal cord, resulting in a thinning versus a resisting response to increasing lumen pressure, respectively. The dorsal hindbrain exhibits reduced apical actin and a disorganized laminin matrix consistent with tissue fluidization. Blocking the activity of neural-crest-associated matrix metalloproteinases inhibits hindbrain expansion. Transplanting dorsal hindbrain cells to the spinal cord can locally create an expanded brain-like morphology in some cases. Our findings raise questions in vertebrate head evolution and suggest a general role of mechanical pre-patterning in sculpting epithelial tubes.
Phase Space Insights: Wigner Functions for Qubits and Beyond
Luis Sanchez-Soto,
Ariana Muñoz,
Pablo de la Hoz,
Andrei B. Klimov,
Gerd Leuchs
Phase space methods, particularly Wigner functions, provide intuitive tools for representing and analyzing quantum states. We focus on systems with SU(2) dynamical symmetry, which naturally describes spin and a wide range of two-mode quantum models. We present a unified phase space framework tailored to these systems, highlighting its broad applicability in quantum optics, metrology, and information. After reviewing the core SU(2) phase-space formalism, we apply it to states designed for optimal quantum sensing, where their nonclassical features are clearly revealed in the Wigner representation. We then extend the approach to systems with an indefinite number of excitations, introducing a generalized framework that captures correlations across multiple SU(2)-invariant subspaces. These results offer practical tools for understanding both theoretical and experimental developments in quantum science.
In physics experiments, mechanical and acoustic vibrations are often considered as disturbing noise and a nuisance. For example, the field of optomechanics came to life because of gravitation waves. To achieve the extreme sensitivity required to detect tiny distortions in spacetime caused by passing gravitational waves, it is crucial to overcome any noisy environments.
Exceptional, but Separate: Precursors to Spontaneous Symmetry Breaking
Lewis Hill,
Julius Gohsrich,
Alekhya Ghosh,
Jacob Fauman,
Pascal Del'Haye,
Flore K. Kunst
Spontaneous symmetry breaking (SSB) and exceptional points (EPs) are often assumed to be inherently linked. Here we investigate the intricate relationship between SSB and specific classes of EPs across three distinct, real-world scenarios in nonlinear optics. In these systems, the two phenomena do not<br>coincide for all classes of EPs; they can occur at dislocated points in parameter space. This recurring behavior across disparate platforms implies that such decoupling is not unique to these optical systems, but likely reflects a more general principle. Our results highlight the need for careful analysis of assumed correlations between SSB and EPs in both theoretical and applied contexts. They deepen our understanding of nonlinear dynamics in<br>optical systems and prompt a broader reconsideration of contexts where EPs and<br>SSB are thought to be interdependent.
Automated Discovery of Coupled Mode Setups
Jonas Landgraf,
Vittorio Peano,
Florian Marquardt
Physical Review X
15
021038
(2025)
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In optics and photonics, a small number of building blocks, like resonators, waveguides, arbitrary couplings, and parametric interactions, allow the design of a broad variety of devices and func- tionalities, distinguished by their scattering properties. These include transducers, amplifiers, and nonreciprocal devices, like isolators or circulators. Usually, the design of such a system is hand- crafted by an experienced scientist in a time-consuming process where it remains uncertain whether the simplest possibility has indeed been found. In our work, we develop a discovery algorithm that automates this challenge. By optimizing the continuous and discrete system properties our auto- mated search identifies the minimal resources required to realize the requested scattering behavior. In the spirit of artificial scientific discovery, it produces a complete list of interpretable solutions and leads to generalizable insights, as we illustrate in several examples. This now opens the door to rapid design in areas like photonic and microwave architectures or optomechanics.
Hybrid Nonlinear Effects in Photonic Integrated Circuits
Nonlinear optics in photonic integrated circuits is usually limited to utilizing the nonlinearity of a single material. In this work, we demonstrate the use of hybrid optical nonlinearities that occur in two different materials. This approach allows us to observe combined Raman scattering and Kerr frequency comb generation using silicon nitride (Si3N4) microresonators with fused silica cladding. Here, the fused silica cladding provides Raman gain, while the silicon nitride core provides the Kerr nonlinearity for frequency comb generation. This way we can add Raman scattering to an integrated photonic silicon nitride platform, in which Raman scattering has not been observed so far because of insufficient Raman gain. The Raman lasing is observed in the silica-clad silicon nitride resonators at an on-chip optical power of 143 mW, which agrees with theoretical simulations. This can be reduced to mw-level with improved optical quality factor. Broadband Raman-Kerr frequency comb generation is realized through dispersion engineering of the waveguides. The use of hybrid optical nonlinearities in multiple materials opens up new functionalities for integrated photonic devices, e.g. by combining second and third-order nonlinear materials for combined supercontinuum generation and self-referencing of frequency combs. Combining materials with low threshold powers for different nonlinearities can be the key to highly efficient nonlinear photonic circuits for compact laser sources, high-resolution spectroscopy, frequency synthesis in the infrared and UV, telecommunications and quantum information processing.
Recent advances in ultrafast lasers enable high-sensitivity, label-free detection of molecular responses in liquids at near-peta hertz frequencies, improving measurement sensitivity and speed in spectroscopy.
Discovering Local Hidden-Variable Models for Arbitrary Multipartite Entangled States and Arbitrary Measurements
Measurement correlations in quantum systems can exhibit non-local behavior, a fundamental aspect of quantum mechanics with applications such as device-independent quantum information processing. However, the explicit construction of local hidden-variable (LHV) models remains an outstanding challenge in the general setting. To address this, we develop an approach that employs gradient-descent algorithms from machine learning to find LHV models which reproduce the statis- tics of arbitrary measurements for quantum many-body states. In contrast to previous approaches, our method employs a general ansatz, enabling it to discover an LHV model in all cases where the state is local. Therefore, it provides actual estimates for the critical noise levels at which two-qubit Werner states and three-qubit GHZ and W states become non-local. Furthermore, we find evidence suggesting that two-spin subsystems in the ground states of translationally invariant Hamiltonians are local, while bigger subsystems are in general not. Our method now offers a quantitative tool for determining the regimes of non-locality in any given physical context, including scenarios involving non-equilibrium and decoherence.
Viscoelastic characterization of cells in microfluidic channels with 3D hydrodynamic focusing
Benedikt Hartmann,
Felix Reichel,
Conrad Möckel,
Jochen Guck
The viscoelastic nature of biological cells has emerged as an increasingly important research subject due to its relevance for cellular functions under physiological and pathological conditions. Advancements in microfluidics have made this technology a promising tool to study the viscoelasticity of cells. However, significant challenges remain, including the complex distribution of stresses acting on cells depending on the channel geometry, and the difficulty of keeping cells in the focal plane for imaging. Here, we report a new approach using hyperbolic channels for measuring cell viscoelasticity. A channel height much larger than the typical cell size minimized shear stresses so that normal stresses in the hyperbolic region dominated the stress distribution. Reducing the complexity of the stress-strain relationship allowed us to use polyacrylamide microgel beads to calibrate the stress curve. Additionally, we introduced 3D hydrodynamic focusing which enabled us to focus cells and microgel beads in the center of the channel. Finally, Kelvin-Voigt and power-law rheology models were employed to extract the mechanical properties of microgel beads and human leukemia HL60 cells. The measurement technique described here will help establish the viscoelastic properties of cells as an important readout in biophysical research in health and disease.
Treatment of acute myeloid leukemia models by targeting a cell surface RNA-binding protein
Benson M. George,
Maria Eleftheriou,
Eliza Yankova,
Jonathan Perr,
Peiyuan Chai,
Gianluca Nestola,
Karim Almahayni,
Siân Evans,
Aristi Damaskou, et al.
Immunotherapies for acute myeloid leukemia (AML) and other cancers are limited by a lack of tumor-specific targets. Here we discover that RNA-binding proteins and glycosylated RNAs (glycoRNAs) form precisely organized nanodomains on cancer cell surfaces. We characterize nucleophosmin (NPM1) as an abundant cell surface protein (csNPM1) on a variety of tumor types. With a focus on AML, we observe csNPM1 on blasts and leukemic stem cells but not on normal hematopoietic stem cells. We develop a monoclonal antibody to target csNPM1, which exhibits robust anti-tumor activity in multiple syngeneic and xenograft models of AML, including patient-derived xenografts, without observable toxicity. We find that csNPM1 is expressed in a mutation-agnostic manner on primary AML cells and may therefore offer a general strategy for detecting and treating AML. Surface profiling and in vivo work also demonstrate csNPM1 as a target on solid tumors. Our data suggest that csNPM1 and its neighboring glycoRNA–cell surface RNA-binding protein (csRBP) clusters may serve as an alternative antigen class for therapeutic targeting or cell identification.
Flocking and giant fluctuations in epithelial active solids
Proceedings of the National Academy of Sciences of the United States of America
122
e2421327122
(2025)
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The collective motion of epithelial cells is a fundamental biological process which plays a significant role in embryogenesis, wound healing, and tumor metastasis. While it has been broadly investigated for over a decade both in vivo and in vitro, large-scale coherent flocking phases remain underexplored and have so far been mostly described as fluid. In this work, we report an additional mode of large-scale collective motion for different epithelial cell types in vitro with distinctive features. By tracking individual cells, we show that cells move over long time scales coherently not as a fluid, but as a polar elastic solid with negligible cell rearrangements. Our analysis reveals that this solid flocking phase exhibits signatures of long-range polar order, accompanying with scale-free correlations of the transverse component of velocity fluctuations, anomalously large density fluctuations, and shear waves. Based on a general theory of active polar solids, we argue that these features result from massless orientational Goldstone mode, which, in contrast to polar fluids where they are generic, require the decoupling of global rotations of the polarity and in-plane elastic deformations in polar solids. We theoretically show and consistently observe in experiments that the fluctuations of elastic deformations diverge for large system sizes in such polar active solid phases, leading eventually to rupture and thus potentially loss of tissue integrity at large scales.
Dynamic expression of laminB1during adult neurogenesis in the vertebrate brain
Diana Zhilina,
Lizbeth A. Bolaños Castro,
Juan Sebastian Eguiguren,
Sara Zocher,
Anne Karasinsky,
Dimitri Widmer,
Alexandre Espinós,
Victor Borrell,
Michael Brand, et al.
Background: In mammals, specific brain regions such as the dentate gyrus (DG) of the hippocampus and the subventricular zone (SVZ) of the lateral ventricles harbor adult neural stem/progenitor cells (ANSPCs) that give rise to new neurons and contribute to structural and functional brain plasticity. In contrast, other vertebrates such as salamanders and zebrafish exhibit a widely distributed neurogenic niches throughout the brain, suggesting a greater neurogenic capacity in adulthood. However, the mechanisms underlying this divergence in neurogenic potential among vertebrates remain elusive. To address this, we examined the expression dynamics of a critical epigenetic regulator for the long-term maintenance of murine ANSPCs, lamin B1, during adult neurogenesis across the vertebrate spectrum. Results: Lamin B1 expression patterns during adult neurogenesis are conserved among mammals including mouse, naked mole-rat, and ferret. However, these patterns differ between mammals and anamniotes. In mammals, neural stem cells and neuroblasts exhibited higher lamin B1 levels, and differentiated neurons possessed lower lamin B1 levels. On the other hand, anamniotes showed the opposite patterns of lamin B1 expression, with higher levels in neurons compared to stem cells. Conclusions: Our study shows that the lamin B1 expression pattern during adult neurogenesis differs between species, and that changes in lamin B1 protein sequence may contribute to the differences in lamin B1 expression patterns. This study highlights potential differences in cell-autonomous epigenetic regulation in the maintenance of ANSPC pools in the adult brain among species.
Digital Discovery of interferometric Gravitational Wave Detectors
Mario Krenn,
Yehonathan Drori,
Rana X Adhikari
Physical Review X
15
021012
(2025)
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Gravitational waves, detected a century after they were first theorized, are space-time distortions caused by some of the most cataclysmic events in the Universe, including black hole mergers and supernovae. The successful detection of these waves has been made possible by ingenious detectors designed by human experts. Beyond these successful designs, the vast space of experimental configurations remains largely unexplored, offering an exciting territory potentially rich in innovative and unconventional detection strategies. Here, we demonstrate an intelligent computational strategy to explore this enormous space, discovering unorthodox topologies for gravitational wave detectors that significantly outperform the currently best-known designs under realistic experimental constraints. This increases the potentially observable volume of the Universe by up to 50-fold. Moreover, by analyzing the best solutions from our superhuman algorithm, we uncover entirely new physics ideas at their core. At a bigger picture, our methodology can readily be extended to AI-driven design of experiments across wide domains of fundamental physics, opening fascinating new windows into the Universe.
Interferometric scattering microscopy
Naomi S. Ginsberg,
Chia-Lung Hsieh,
Philipp Kukura,
Marek Piliarik,
Vahid Sandoghdar
Over the past two decades, interferometric scattering (iSCAT) microscopy has become a powerful label-free imaging method with a range of applications in fundamental science and technology. iSCAT detects the scattering of subwavelength entities through interference with a reference beam of light. Performed in a variety of illumination and detection schemes, iSCAT has exploited both amplitude and phase information to reach single-molecule detection sensitivity; to determine the size, mass and refractive index of nanoparticles; to achieve high spatiotemporal precision in 3D tracking of nanoparticles; to image subcellular nanostructures; and to quantify ultrafast diffusion and transport of energy in solids. In this Primer, we describe the basic principles of iSCAT detection and imaging from theoretical and practical points of view. We discuss various factors that affect the attainable signal-to-noise ratio, which in turn determines crucial performance features such as sensitivity and speed. We survey selected applications in which iSCAT has been instrumental in providing new insights. Finally, we discuss some of the current challenges and potential avenues for advancing the technique further.
Exceptional Points and Stability in Nonlinear Models of Population Dynamics having PT symmetry
Alexander Felski,
Flore K. Kunst
Physical Review Research
7
013326
(2025)
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Nonlinearity and non-Hermiticity, for example due to environmental gain-loss processes, are a common occurrence throughout numerous areas of science and lie at the root of many remarkable phenomena. For the latter, parity-time-reflection PT symmetry has played an eminent role in understanding exceptional-point structures and phase transitions in these systems. Yet their interplay has remained by-and-large unexplored. We analyze models governed by the replicator equation of evolutionary game theory and related Lotka-Volterra systems of population dynamics. These are foundational nonlinear models that find widespread application and offer a broad platform for non-Hermitian theory beyond physics. In this context we study the emergence of exceptional points in two cases: (a) when the governing symmetry properties are tied to global properties of the models, and, in contrast, (b) when these symmetries emerge locally around stationary states - in which case the connection between the linear non-Hermitian model and an underlying nonlinear system becomes tenuous. We outline further that when the relevant symmetries are related to global properties, the location of exceptional points in the linearization around coexistence equilibria coincides with abrupt global changes in the stability of the nonlinear dynamics. Exceptional points may thus offer a new local characteristic for the understanding of these systems. Tri-trophic models of population ecology serve as test cases for higher-dimensional systems.
Selective plane illumination microscopy (SPIM), also known as light sheet fluorescence microscopy, provides high specificity through fluorescence labeling. However, it lacks complementary structural information from the surrounding context, which is essential for the comprehensive analysis of biological samples. Here, we present a high-resolution, multimodal imaging system that integrates SPIM with full-field optical coherence tomography (FF-OCT), without requiring modifications to the existing SPIM setup. Both SPIM and FF-OCT offer low phototoxicity and intrinsic optical sectioning, making them well-suited for in vivo imaging. Their shared detection path enables seamless and efficient co-registration of fluorescence and structural data. We demonstrate the functionality of this combined system by performing in vivo imaging of zebrafish larvae.
Resolving spatiotemporal dynamics in bacterial multicellular populations: approaches and challenges
Suyen Solange Espinoza Miranda,
Gorkhmaz Abbaszade,
Wolfgang R. Hess,
Knut Drescher,
Antoine-Emmanuel Saliba,
Vasily Zaburdaev,
Liraz Chai,
Klaus Dreisewerd,
Alexander Grünberger, et al.
Microbiology and Molecular Biology Reviews
89
e00138-24
(2025)
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The development of multicellularity represents a key evolutionary transition that is crucial for the emergence of complex life forms. Although multicellularity has traditionally been studied in eukaryotes, it originates in prokaryotes. Coordinated aggregation of individual cells within the confines of a colony results in emerging, higher-level functions that benefit the population as a whole. During colony differentiation, an almost infinite number of ecological and physiological population-forming forces are at work, creating complex, intricate colony structures with divergent functions. Understanding the assembly and dynamics of such populations requires resolving individual cells or cell groups within such macroscopic structures. Addressing how each cell contributes to the collective action requires pushing the resolution boundaries of key technologies that will be presented in this review. In particular, single-cell techniques provide powerful tools for studying bacterial multicellularity with unprecedented spatial and temporal resolution. These advancements include novel microscopic techniques, mass spectrometry imaging, flow cytometry, spatial transcriptomics, single-bacteria RNA sequencing, and the integration of spatiotemporal transcriptomics with microscopy, alongside advanced microfluidic cultivation systems. This review encourages exploring the synergistic potential of the new technologies in the study of bacterial multicellularity, with a particular focus on individuals in differentiated bacterial biofilms (colonies). It highlights how resolving population structures at the single-cell level and understanding their respective functions can elucidate the overarching functions of bacterial multicellular populations.
Solving Dicke superradiance analytically: A compendium of methods
Raphael Holzinger,
Nico S. Baßler,
Julian Lyne,
Fidel G. Jimenez,
Julius Gohsrich,
Claudiu Genes
We present several analytical approaches to the Dicke superradiance problem, which involves determining the time evolution of the density operator for an initially inverted ensemble of $N$ identical two-level systems undergoing collective spontaneous emission. This serves as one of the simplest cases of open quantum system dynamics that allows for a fully analytical solution. We explore multiple methods to tackle this problem, yielding a solution valid for any time and any number of spins. These approaches range from solving coupled rate equations and identifying exceptional points in non-Hermitian evolution to employing combinatorial and probabilistic techniques, as well as utilizing a quantum jump unraveling of the master equation. The analytical solution is expressed as a residue sum obtained from a contour integral in the complex plane, suggesting the possibility of fully analytical solutions for a broader class of open quantum system dynamics problems.
A simple model for entangled photon generation in resonant structures
Nicholas J. Sorensen,
Vitaliy Sultanov,
Maria Chekhova
Optics Express
33
13946-13960
(2025)
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The ability to engineer pairs of entangled photons is essential to quantum information science, and generating these states using spontaneous parametric down-conversion (SPDC) in nano- and micrometer-scale materials offers numerous advantages. To properly engineer such sources, a reliable model describing nano- and micrometer-scale SPDC is necessary; however, such a theoretical description remains a challenge. Here, we propose and derive a simplified model to describe SPDC in resonant structures, which considers the generation of photon pairs and the resonant enhancement of spectral bands to be separate processes, even though they actually occur simultaneously. We compare our simplified model to both the rigorous theory of SPDC in an etalon – a simple example of a resonant structure – and our experiments on SPDC in etalons and find agreement for low-gain SPDC. By simplifying the calculations required to generate photon pairs, our model promises to make designing complex resonant structures easier, and it promises to hasten the iteration of designs across the field of quantum state engineering.
Transfer learning in predicting quantum many-body dynamics: from physical observables to entanglement entropy
Philipp Schmidt,
Florian Marquardt,
Naeimeh Mohseni
Quantum Science and Technology
10
025038
(2025)
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Deep neural networks have demonstrated remarkable efficacy in extracting meaningful representations from complex datasets. This has propelled representation learning as a compelling area of research across diverse fields. One interesting open question is how beneficial representation learning can be for quantum many-body physics, with its notoriously high-dimensional state space. In this work, we showcase the capacity of a neural network that was trained on a subset of physical observables of a many-body system to partially acquire an implicit representation of the wave function. We illustrate this by demonstrating the effectiveness of reusing the representation learned by the neural network to enhance the learning process of another quantity derived from the quantum state. In particular, we focus on how the pre-trained neural network can enhance the learning of entanglement entropy. This is of particular interest as directly measuring the entanglement in a many-body system is very challenging, while a subset of physical observables can be easily measured in experiments. We show the pre-trained neural network learns the dynamics of entropy with fewer resources and higher precision in comparison with direct training on the entanglement entropy.
Measurement force, speed, and postmortem time affect the ratio of CNS gray-to-white-matter elasticity
Julia Monika Becker,
Alexander Kevin Winkel,
Eva Kreysing,
Kristian Franze
For several decades, many attempts have been made to characterize the mechanical properties of gray and white matter, which constitute the two main compartments of the central nervous system, with various methods and contradictory results. In particular, the ratio of gray-to-white-matter elasticity is sometimes larger than 1 and sometimes smaller; the reason for this apparent discrepancy is currently unknown. Here, we exploited atomic force microscopy-based indentation measurements to systematically investigate how the measurement force, measurement speed, postmortem interval, and temperature affect the measured elasticity of spinal cord tissue and, in particular, the ratio of gray-to-white-matter elasticity (Kg/Kw). Within the explored parameter space, increasing measurement force and speed increased the measured elasticity of both gray and white matter. However, Kg/Kw declined from values as high as ∼5 at low forces and speeds to ∼1 for high forces and speeds. Kg/Kw also strongly depended on the anatomical plane in which the measurements were conducted and was considerably higher in transverse sections compared with longitudinal sections. Furthermore, the postmortem interval impacted both the absolute measured tissue elasticity and Kg/Kw. Gray matter elasticity started decreasing ∼3 h postmortem until reaching a plateau after ∼6 h. In contrast, white matter elasticity started declining from the beginning of the measurements until ∼6 h postmortem, when it also leveled off. As a result, Kg/Kw increased until ∼6 h postmortem before stabilizing. Between 20 and 38°C, both gray and white matter elasticity decreased at a similar rate without affecting Kg/Kw. We have thus identified differences in the response of gray and white matter to varying strains and strain rates, and the postmortem interval, and excluded temperature as a factor affecting Kg/Kw. These differential responses likely contribute to the contradictory results obtained with different methods working in different strain regimes.
in Press
Force transmission is a master regulator of mechanical cell competition
Andreas Schoenit,
Siavash Monfared,
Lucas Anger,
Carine Rosse,
Varun Venkatesh,
Lakshmi Balasubramaniam,
Elisabetta Marangoni,
Philippe Chavrier,
René-Marc Mège, et al.
Nature Materials
24
966-976
(2025)
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Cell competition is a tissue surveillance mechanism for eliminating unwanted cells, being indispensable in development, infection and tumourigenesis. Although studies have established the role of biochemical mechanisms in this process, due to challenges in measuring forces in these systems, how mechanical forces determine the competition outcome remains unclear. Here we report a form of cell competition that is regulated by differences in force transmission capabilities, selecting for cell types with stronger intercellular adhesion. Direct force measurements in ex vivo tissues and different cell lines reveal that there is an increased mechanical activity at the interface between two competing cell types, which can lead to large stress fluctuations resulting in upward forces and cell elimination. We show how a winning cell type endowed with a stronger intercellular adhesion exhibits higher resistance to elimination and benefiting from efficient force transmission to the neighbouring cells. This cell elimination mechanism could have broad implications for keeping the strong force transmission ability for maintaining tissue boundaries and cell invasion pathology.
Scaling the Automated Discovery of Quantum Circuits via Reinforcement Learning with Gadgets
Jan Ollé Aguilera,
Oleg M. Yevtushenko,
Florian Marquardt
Reinforcement Learning (RL) has established itself as a powerful tool for designing quantum circuits, which are essential for processing quantum information. RL applications have typically focused on circuits of small to intermediate complexity, as computation times tend to increase exponentially with growing circuit complexity. This computational explosion severely limits the scalability of RL and casts significant doubt on its broader applicability. In this paper, we propose a principled approach based on the systematic discovery and introduction of composite gates – gadgets, that enables RL scalability, thereby expanding its potential applications. As a case study, we explore the discovery of Clifford encoders for Quantum Error Correction. We demonstrate that incorporating gadgets in the form of composite Clifford gates, in addition to standard CNOT and Hadamard gates, significantly enhances the efficiency of RL agents. Specifically, the computation speed increases (by one or even two orders of magnitude), enabling RL to discover highly complex quantum codes without previous knowledge. We illustrate this advancement with examples of QEC code discovery with parameters [[n, 1, d]] for d ≤ 7 and [[n, k, 6]] for k ≤ 7. We note that the most complicated circuits of these classes were not previously found. We highlight the advantages and limitations of the gadget-based approach. Our method paves the way for scaling the RL-based automatic discovery of complicated quantum circuits for various tasks, which may include designing logical operations between logical qubits or discovering quantum algorithms.
Meta-learning characteristics and dynamics of quantum systems
Lucas Schorling,
Pranav Vaidhyanathan,
Jonas Schuff,
Miguel J. Carballido,
Dominik Zumbühl,
Gerard Milburn,
Florian Marquardt,
Jakob Foerster,
Michael A. Osborne, et al.
While machine learning holds great promise for quantum technologies, most current methods fo- cus on predicting or controlling a specific quantum system. Meta-learning approaches, however, can adapt to new systems for which little data is available, by leveraging knowledge obtained from previ- ous data associated with similar systems. In this paper, we meta-learn dynamics and characteristics of closed and open two-level systems, as well as the Heisenberg model. Based on experimental data of a Loss-DiVincenzo spin-qubit hosted in a Ge/Si core/shell nanowire for different gate voltage config- urations, we predict qubit characteristics i.e. g-factor and Rabi frequency using meta-learning. The algorithm we introduce improves upon previous state-of-the-art meta-learning methods for physics- based systems by introducing novel techniques such as adaptive learning rates and a global optimizer for improved robustness and increased computational efficiency. We benchmark our method against other meta-learning methods, a vanilla transformer, and a multilayer perceptron, and demonstrate improved performance.
Squeezing via self-induced transparency in mercury-filled photonic crystal fibers
M. S. Najafabadi,
J. F. Corney,
L. L. Sanchez-Soto,
N. Y. Joly,
G. Leuchs
Journal of the Optical Society of America B-Optical Physics
42
749-756
(2025)
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We investigate the squeezing of ultrashort pulses using self-induced transparency in a mercury-filled hollow-core photonic crystal fiber. Our focus is on quadrature squeezing at low mercury vapor pressures, with atoms near resonance on the 3D3->63P2 transition. We vary the atomic density, and thus the gas pressure (from 2.72 to 15.7 µbar), by adjusting the temperature (from 273 to 303 K). Our results show that achieving squeezing at room temperature, considering both fermionic and bosonic mercury isotopes, requires ultrashort femtosecond pulses. We also determine the optimal detection length for squeezing at different pressures and temperatures.
Longitudinal Monitoring of Inflammatory Bowel Disease in Mice Using Endoscopic Optical Coherence Tomography
Muktesh Mohan,
Oana-Maria Thoma,
Shivani Sharma,
Gargi Sharma,
Markus F Neurath,
Maximilian Waldner,
Kanwarpal Singh
Inflammatory Bowel Diseases
izaf045
(2025)
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Background: Inflammatory bowel disease (IBD) is one of the fastest-growing diseases globally. Nearly 5 million people are affected by IBD, with an incremental growth rate of 47.45% between 1990 and 2019. Aim and Methods: We aim to provide a noninvasive approach to detecting IBD with an in-house developed 1310 nm endoscopic optical coherence tomography (OCT) system. Mice with acute colitis underwent a longitudinal colon imaging process for real-time and long-run disease progression. The OCT images were processed and segmented using a computer vision image processing-based segmentation algorithm for further thickness mapping and attenuation coefficient calculations. Result: An increase in overall colon wall thickness due to inflammation was observed, as well as a reduction in attenuation coefficient due to a change in refractive index. Conclusion: Comparable results with white light endoscope and histological examination suggest the clinical potential of the 1310 nm endoscopic OCT system for in vivo assessment of IBD.
Femtosecond Fieldoscopy for super-resolution label-free microscopy
Soyeon Jun,
Andreas Herbst,
Kilian Scheffter,
Daniel Wehner,
Anchit Srivastava,
Hanieh Fattahi
Accessing complete electric field information of a laser pulse interacting with a medium at visible to near-infrared (near-petahertz) frequencies has traditionally required complex laboratory systems operating in vacuum conditions. Recent advancements, however, have enabled the measurement of electric fields at near-petahertz frequencies in ambient air. This capability is critical for understanding ultrafast phenomena and for achieving quantitative detection of molecular species in various samples. This article introduces Femtosecond Fieldoscopy, a field-resolved detection technique for label-free spectroscopy and microscopy. This approach delivers exceptional detection sensitivity and dynamic range at petahertz bandwidths by combining attosecond temporal resolution with temporal isolation of target molecular responses from environmental and excitation pulse effects. Furthermore, Femtosecond Fieldoscopy holds promise for achieving sub-diffraction spatial resolution, opening new horizons for high-precision label-free spectro-microscopy.
Inequality-free proof of Bell's theorem
Andrea Aiello
Physical Review A
111
032204
(2025)
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Many of the tests of local realistic hidden-variable theories against quantum mechanics are based on inequalities such as Bell's inequality and Clauser Horne, Shimony, and Holt's inequality. In this work we present a simple alternative test which does not involve inequalities, but direct comparison between correlation functions. The main advantage of this test is that it does not require measuring incompatible observables separately and simultaneously. This implies that our result, differently from traditional inequalities, does not involve counterfactual reasoning.
Electrostatic All-Passive Force Clamping of Charged Nanoparticles
Yazgan Tuna,
Amer Al-Hiyasat,
Anna D. Kashkanova,
Andreas Dechant,
Eric Lutz,
Vahid Sandoghdar
In the past decades, many techniques have been explored for trapping microscopic and nanoscopic objects, but the investigation of nano-objects under arbitrary forces and conditions remains nontrivial. One fundamental case concerns the motion of a particle under a constant force, known as force clamping. Here, we employ metallic nanoribbons embedded in a glass substrate in a capacitor configuration to generate a constant electric field on a charged nanoparticle in a water-filled glass nanochannel. We estimate the force fields from Brownian trajectories over several micrometers and confirm the constant behavior of the forces both numerically and experimentally. Furthermore, we manipulate the diffusion and relaxation times of the nanoparticles by tuning the charge density on the electrode. Our highly compact and controllable setting allows for the trapping and force-clamping of charged nanoparticles in a solution, providing a platform for investigating nanoscopic diffusion phenomena.
Predicting atmospheric turbulence for secure quantum communications in free space
Tareq Jaouni,
Lukas Scarfe,
Frédéric Bouchard,
Mario Krenn,
Khabat Heshami,
Francesco Di Colandrea,
Ebrahim Karimi
Optics Express
33
10759-10776
(2025)
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Atmospheric turbulence is the main barrier to large-scale free-space quantum communication networks. Aberrations distort optical information carriers, thus limiting or preventing the possibility of establishing a secure link between two parties. For this reason, forecasting the turbulence strength within an optical channel is highly desirable, as it allows for knowing the optimal timing to establish a secure link in advance. Here, we train a recurrent neural network, TAROQQO, to predict the turbulence strength within a free-space channel. The training is based on weather and turbulence data collected over 9 months for a 5.4 km intra-city free-space link across the City of Ottawa. The implications of accurate predictions from our network are demonstrated in a simulated high-dimensional quantum key distribution protocol based on orbital angular momentum states of light across different turbulence regimes. TAROQQO will be crucial in validating a free-space channel to optimally route the key exchange for secure communications in real experimental scenarios.
Modelling spectra of hot alkali vapour in the saturation regime
Daniel Häupl,
Clare R Higgins,
Danielle Pizzey,
Jack D Briscoe,
Steven A Wrathmall,
Ifan G Hughes,
Robert Löw,
Nicolas Y. Joly
New Journal of Physics
27
033003
(2025)
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Laser spectroscopy of hot atomic vapours has been studied extensively. Theoretical models that predict the absolute value of the electric susceptibility are crucial for optimising the design of photonic devices that use hot vapours, and for extracting parameters, such as external fields, when these devices are used as sensors. To date, most of the models developed have been restricted to the weak-probe regime. However, fulfilling the weak-probe power constraint may not always be easy, desired or necessary. Here we present a model for simulating the spectra of alkali-metal vapours for a variety of experimental parameters, most distinctly at intensities beyond weak laser fields. The model incorporates optical pumping effects and transit-time broadening. We test the performance of the model by performing spectroscopy of 87Rb in a magnetic field of 0.6 T, where isolated atomic resonances can be addressed. We find very good agreement between the model and data for three different beam diameters and a variation of intensity of over five orders of magnitude. The non-overlapping absorption lines allow us to differentiate the saturation behaviour of open and closed transitions. While our model was only experimentally verified for the D2 line of rubidium, the software is also capable of simulating spectra of rubidium, sodium, potassium and caesium over both D lines.
RNA binding proteins and glycoRNAs form domains on the cell surface for cell penetrating peptide entry
Jonathan Perr,
Andreas Langen,
Karim Almahayni,
Gianluca Nestola,
Peiyuan Chai,
Charlotta G. Lebedenko,
Regan Volk,
Reese M. Caldwell,
Malte Spiekermann, et al.
The composition and organization of the cell surface determine how cells interact with their environment. Traditionally, glycosylated transmembrane proteins were thought to be the major constituents of the external surface of the plasma membrane. Here, we provide evidence that a group of RNA-binding proteins (RBPs) is present on the surface of living cells. These cell-surface RBPs (csRBPs) precisely organize into well-defined nanoclusters enriched for multiple RBPs and glycoRNAs, and their clustering can be disrupted by extracellular RNase addition. These glycoRNA-csRBP clusters further serve as sites of cell-surface interaction for the cell-penetrating peptide trans-activator of transcription (TAT). Removal of RNA from the cell surface, or loss of RNA-binding activity by TAT, causes defects in TAT cell internalization. Together, we provide evidence of an expanded view of the cell surface by positioning glycoRNA-csRBP clusters as a regulator of communication between cells and the extracellular environment.
dCG—differentiable connected geometries for AI-compatible multi-domain optimization and inverse design
Alexander Luce,
Daniel Grünbaum,
Florian Marquardt
Machine Learning: Science and Technology
6
015055
(2025)
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In the domain of geometry and topology optimization, discovering geometries that optimally satisfy specific problem criteria is a complex challenge in both engineering and scientific research. In this work, we propose a new approach for the creation of multidomain connected geometries that are designed to work with automatic differentiation. We introduce the concept of differentiable Connected Geometries (dCG), discussing its theoretical aspects and illustrating its application through a simple toy examples and a more sophisticated photonic optimization task. Since these geometries are built upon the principles of automatic differentiation, they are compatible with existing deep learning frameworks, a feature we demonstrate via the application examples. This methodology provides a systematic way to approach geometric design and optimization in computational fields involving dependent geometries, potentially improving the efficiency and effectiveness of optimization tasks in scientific and engineering applications.
Neural-Network-Based Selective Configuration Interaction Approach to Molecular Electronic Structure
Yorick L. A. Schmerwitz,
Louis Thirion,
Gianluca Levi,
Elvar Ö. Jónsson,
Pavlo Bilous,
Hannes Jónsson,
Philipp Hansmann
Journal of Chemical Theory and Computation
21
2301-2310
(2025)
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By combining Hartree–Fock with a neural-network-supported quantum-cluster solver proposed recently in the context of solid-state lattice models, we formulate a scheme for selective neural-network configuration interaction (NNCI) calculations and implement it with various options for the type of basis set and boundary conditions. The method’s performance is evaluated in studies of several small molecules as a step toward calculations of larger systems. In particular, the correlation energy in the N₂ molecule is compared with published full CI calculations that included nearly 10¹⁰ Slater determinants, and the results are reproduced with only 4 × 10⁵ determinants using NNCI. A clear advantage is seen from increasing the set of orbitals included rather than approaching full CI for a smaller set. The method’s high efficiency and implementation in a condensed matter simulation software expands the applicability of CI calculations to a wider range of problems, even extended systems through an embedding approach.
SOLAX: A Python solver for fermionic quantum systems with neural network
support
Numerical modeling of fermionic many-body quantum systems presents similar challenges across various research domains, necessitating universal tools, including state-of-the-art machine learning techniques. Here, we introduce SOLAX, a Python library designed to compute and analyze fermionic quantum systems using the formalism of second quantization. SOLAX provides a modular framework for constructing and manipulating basis sets, quantum states, and operators, facilitating the simulation of electronic structures and determining many-body quantum states in finite-size Hilbert spaces. The library integrates machine learning capabilities to mitigate the exponential growth of Hilbert space dimensions in large quantum clusters. The core low-level functionalities are implemented using the recently developed Python library JAX. Demonstrated through its application to the Single Impurity Anderson Model, SOLAX offers a flexible and powerful tool for researchers addressing the challenges of many-body quantum systems across a broad spectrum of fields, including atomic physics, quantum chemistry, and condensed matter physics.
Near‐Infrared Dual‐Band Frequency Comb Generation from a Silicon Resonator
Keyi Zhong,
Yaojing Zhang,
Shuangyou Zhang,
Yuanfei Zhang,
Yuan Li,
Yue Qin,
Yi Wang,
Jose M. Chavez Boggio,
Xiankai Sun, et al.
Benefitting from the mature, cost-effective, and scalable manufacturing capabilities of complementary metal-oxide-semiconductor (CMOS) technology, silicon photonics has facilitated the seamless and monolithic integration of diverse functionalities, including optical sources, modulators, and photodetectors. Microresonators can generate multiple coherent optical frequency comb lines and serve as optical sources. However, at the telecom band, silicon suffers from two-photon absorption and free-carrier absorption, which severely hampers the realization of microcombs from a single silicon chip at telecom wavelengths until now. In this paper, a novel approach is presented and demonstrated with near-infrared dual-band frequency combs from a multimode silicon resonator. With a single pumping configuration, dual-band combs are generated from the interaction between the pump and Raman Stokes fields by involving two different optical mode families but with similar group velocities. It is observed that the pump power required to generate dual-band combs is as low as 0.7 mW. The work in bringing telecom microcombs to the silicon platform will advance silicon photonics for the next generation of monolithically integrated technology based on a single silicon chip, enabling new possibilities for further exploring silicon photonics-based applications in optical telecommunications, sensing, and quantum metrology in the telecom band using a monolithic single silicon chip.
De novo identification of universal cell mechanics gene signatures
Marta Urbanska,
Yan Ge,
Maria Winzi,
Shada Abuhattum Hofemeier,
Syed Shafat Ali,
Maik Herbig,
Martin Kräter,
Nicole Toepfner,
Joanne Durgan, et al.
Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here, we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy, and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way toward engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.
All-optical nonlinear activation function based on stimulated Brillouin scattering
Grigorii Slinkov,
Steven Becker,
Dirk Englund,
Birgit Stiller
Optical neural networks have demonstrated their potential to overcome the computational bottleneck of modern digital electronics. However, their development towards high-performing computing alternatives is hindered by one of the optical neural networks’ key components: the activation function. Most of the reported activation functions rely on opto-electronic conversion, sacrificing the unique advantages of photonics, such as resource-efficient coherent and frequency-multiplexed information encoding. Here, we experimentally demonstrate a photonic nonlinear activation function based on stimulated Brillouin scattering. It is coherent and frequency selective and can be tuned all-optically to take LEAKYRELU, SIGMOID, and QUADRATIC shape. Our design compensates for the insertion loss automatically by providing net gain as high as 20 dB, paving the way for deep optical neural networks.
Massive quantum systems as interfaces of quantum mechanics and gravity
Sougato Bose,
Ivette Fuentes,
Andrew A. Geraci,
Saba Mehsar Khan,
Sofia Qvarfort,
Markus Rademacher,
Muddassar Rashid,
Marko Toroš,
Hendrik Ulbricht, et al.
Reviews of Modern Physics
97
015003
(2025)
| Journal
The traditional view from particle physics is that quantum-gravity effects should become detectable only at extremely high energies and small length scales. Owing to the significant technological challenges involved, there has been limited progress in identifying experimentally detectable effects that can be accessed in the foreseeable future. However, in recent decades, the size and mass of quantum systems that can be controlled in the laboratory have reached unprecedented scales, enabled by advances in ground-state cooling and quantum-control techniques. Preparations of massive systems in quantum states pave the way for the exploration of a low-energy regime in which gravity can be both sourced and probed by quantum systems. Such approaches constitute an increasingly viable alternative to accelerator-based, laser-interferometric, torsion-balance, and cosmological tests of gravity. This review provides an overview of proposals where massive quantum systems act as interfaces between quantum mechanics and gravity. Conceptual difficulties in the theoretical description of quantum systems in the presence of gravity are discussed, tools for modeling massive quantum systems in the laboratory are reviewed, and an overview of the current state-of-the-art experimental landscape is provided. Proposals covered in this review include precision tests of gravity, tests of gravitationally induced wave-function collapse and decoherence, and gravity-mediated entanglement. The review concludes with an outlook and a summary of the key questions raised.
Ultra-broadband UV/VIS spectroscopy enabled by resonant dispersive wave emission of a frequency comb
Adrian Kirchner,
Alexander Eber,
Lukas Fürst,
Emily Hruska,
Michael Frosz,
Francesco Tani,
Birgitta Bernhardt
Optics Express
33
7005-7015
(2025)
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We introduce an agile light source bridging from the near ultraviolet to the visible spectral region by covering more than 240 THz through resonant dispersive wave (RDW) emission in a gas-filled hollow-core fiber waveguide. The light source allows tuning of a 20 nm (FWHM) spectrum from ∼340 nm to 465 nm (645 to ∼885 THz) with conversion efficiencies of (1.5 ± 0.4) %, providing spectral powers up to (2.6 ± 1) mW/nm. This technique is showcased for spectroscopy with broadband absorption measurements of nitrogen dioxide, a molecular species of major atmospheric relevance. To our knowledge, this is the first demonstration of absorption spectroscopy with an RDW-based light source. The presented measurements indicate conservation of the coherence of the frequency comb seeding the frequency up-conversion process, paving the way towards ultra-broadband (dual) comb molecular spectroscopy across the highly relevant ultraviolet and visible range.
Giant Helical Dichroism in Twisted Hollow-Core Photonic Crystal Fibers
We show that twisted single-ring hollow-core fibers can exhibit strong helical dichroism, i.e., a different transmission depending on the orbital angular momentum of the launched light. Experimentally, we observe loss differences of at least 40 dB/m over a broad spectral range (>60 THz). We investigate the effect via analytical and numerical studies and show that considerably higher differential loss can be achieved over a broader spectral range (>180 THz). Our observation provides new routes for controlling the polarization state, extends previous studies of circularly dichroic waveguides, and has many potential applications, such as the realization of new polarizing elements in previously inaccessible spectral regions, chiral sensing, broadband generation of vortex beams, and optical communication.
Discovering emergent connections in quantum physics research via dynamic word embeddings
Felix Frohnert,
Xuemei Gu,
Mario Krenn,
Evert van Nieuwenburg
Machine Learning: Science and Technology
6
015029
(2025)
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| PDF
As the field of quantum physics evolves, researchers naturally form subgroups focusing on structurally similar problems in different subfields. While this encourages in-depth exploration, it can also limit the exchange of ideas. To encourage cross-talk among these specialized areas, data-driven approaches using machine learning have recently shown promise in uncovering meaningful connections between research concepts, promoting cross-disciplinary innovation. Current state-of-the-art approaches represent concepts using knowledge graphs and frame the task as a link prediction problem, where connections between concepts are explicitly modeled. In this work, we introduce a novel approach based on dynamic word embeddings for concept combination prediction. Unlike knowledge graphs, our method captures implicit relationships between concepts, can be learned in a fully unsupervised manner, and encapsulates a broader spectrum of information. We demonstrate that our representation enables accurate predictions of the co-occurrence of concepts within research abstracts over time. Furthermore, we provide a comprehensive benchmark comparing our method against existing approaches and offer insights into the interpretability of these embeddings, particularly in the context of quantum physics research. Our findings suggest that this representation offers a more flexible and informative way of modeling conceptual relationships in scientific literature.
An optical coherence tomography study of a photoactive Pt(iv) prodrug in oesophageal tissue
Huayun Shi,
Muktesh Mohan,
Kanwarpal Singh,
Peter J. Sadler
Photoactive diazido Pt(IV) complexes display in vivo anticancer efficacy towards oesophageal tumours, a worldwide common cancer. Here we explore the use of optical coherence tomography (OCT) as a new method for detecting tissue penetration and damage produced by the photoactivatable anticancer complex trans,trans,trans-[Pt(pyridine)2(N3)2(OH)2] (FM190). Dehydration of the sample and a change in refractive index were observed for swine oesophageal tissue treated with FM190 and blue laser light (445 nm) using an OCT system. In contrast, tissues treated with FM190 or laser light alone showed no apparent damage.
Simultaneous measurement of multimode squeezing through multimode phase-sensitive amplification
Ismail Barakat,
Mahmoud Kalash,
Dennis Scharwald,
Polina Sharapova,
Norbert Lindlein,
Maria Chekhova
Multimode squeezed light is increasingly popular in photonic quantum technologies, including sensing, imaging, and computation. Existing methods for its characterization are technically complex, often reducing the level of squeezing and typically addressing only a single mode at a time. Here, for the first time, we employ optical parametric amplification to characterize multiple squeezing eigenmodes simultaneously. We retrieve the shapes and squeezing degrees of all modes at once through direct detection followed by modal decomposition. This method is tolerant to inefficient detection and does not require a local oscillator. For a spectrally and spatially multimode squeezed vacuum, we characterize the eight strongest spatial modes, obtaining squeezing and anti-squeezing values of up to −5.2 ± 0.2 dB and 8.6 ± 0.3 dB, respectively, despite 50% detection loss. This work, being the first exploration of an optical parametric amplifier’s multimode capability for squeezing detection, paves the way for real-time multimode squeezing detection.
From Dyson Models to many-body Quantum Chaos
Alexei Andreanov,
Matteo Carrega,
Jeff Murugan,
Jan Olle,
Dario Rosa,
Ruth Shir
A deep understanding of the mechanisms underlying many-body quantum chaos is one of the big challenges in contemporary theoretical physics. We tackle this problem in the context of a set of perturbed quadratic Sachdev-Ye-Kitaev (SYK) Hamiltonians defined on graphs. This allows us to disentangle the geometrical properties of the underlying single-particle problem and the importance of the interaction terms, showing that the former is the dominant feature ensuring the single-particle to many-body chaotic transition. Our results are verified numerically with state-of-the-art numerical techniques, capable of extracting eigenvalues in a desired energy window of very large Hamiltonians. Our approach essentially provides a new way of viewing many-body chaos from a single-particle perspective.
Integrated optical switches based on Kerr symmetry breaking in microresonators
Yaojing Zhang,
Shuangyou Zhang,
Alekhya Ghosh,
Arghadeep Pal,
George N. Ghalanos,
Toby Bi,
Haochen Yan,
Hao Zhang,
Yongyong Zhuang, et al.
Photonics Research
13
360-366
(2025)
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With the rapid development of the Internet of Things and big data, integrated optical switches are gaining prominence for applications in on-chip optical computing, optical memories, and optical communications. Here, we propose a novel approach for on-chip optical switches by utilizing the nonlinear optical Kerr effect induced spontaneous symmetry breaking (SSB), which leads to two distinct states of counterpropagating light in ring resonators. This technique is based on our first experimental observation of on-chip symmetry breaking in a high-Q (9.4 × 106) silicon nitride resonator with a measured SSB threshold power of approximately 3.9 mW. We further explore the influence of varying pump powers and frequency detunings on the performance of SSB-induced optical switches. Our work provides insights into the development of new types of photonic data processing devices and provides an innovative approach for the future implementation of on-chip optical memories.
On-chip microresonator dispersion engineering via segmented sidewall modulation
Masoud Kheyri,
Shuangyou Zhang,
Toby Bi,
Arghadeep Pal,
Hao Zhang,
Yaojing Zhang,
Abdullah Alabbadi,
Haochen Yan,
Alekhya Ghosh, et al.
Photonics Research
13
367-372
(2025)
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Microresonator dispersion plays a crucial role in shaping the nonlinear dynamics of microcavity solitons. Here, we introduce and validate a method for dispersion engineering through modulating a portion of the inner edge of ring waveguides. We demonstrate that such partial modulation has a broadband effect on the dispersion profile, whereas modulation on the entire resonator’s inner circumference leads to mode splitting primarily affecting one optical mode. The impact of spatial modulation amplitude, period, and number of modulations on the mode splitting profile is also investigated. Through the integration of four modulated sections with different modulation amplitudes and periods, we achieve mode splitting across more than 50 modes over a spectral range exceeding 100 nm in silicon nitride resonators. These results highlight both the simplicity and efficacy of our method in achieving flatter dispersion profiles.
Lipidic folding pathway of α-Synuclein via a toxic oligomer
Vrinda Sant,
Dirk Matthes,
Hisham Mazal,
Leif Antonschmidt,
Franz Wieser,
Kumar Tekwani Movellan,
Kai Xue,
Evgeny Nimerovsky,
Marianna Stampolaki, et al.
Nature Communications
16
760
(2025)
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Aggregation intermediates play a pivotal role in the assembly of amyloid fibrils, which are central to the pathogenesis of neurodegenerative diseases. The structures of filamentous intermediates and mature fibrils are now efficiently determined by single-particle cryo-electron microscopy. By contrast, smaller pre-fibrillar α-Synuclein (αS) oligomers, crucial for initiating amyloidogenesis, remain largely uncharacterized. We report an atomic-resolution structural characterization of a toxic pre-fibrillar aggregation intermediate (I1) on pathway to the formation of lipidic fibrils, which incorporate lipid molecules on protofilament surfaces during fibril growth on membranes. Super-resolution microscopy reveals a tetrameric state, providing insights into the early oligomeric assembly. Time-resolved nuclear magnetic resonance (NMR) measurements uncover a structural reorganization essential for the transition of I1 to mature lipidic L2 fibrils. The reorganization involves the transformation of anti-parallel β-strands during the pre-fibrillar I1 state into a β-arc characteristic of amyloid fibrils. This structural reconfiguration occurs in a conserved structural kernel shared by a vast number of αS-fibril polymorphs, including extracted fibrils from Parkinson’s and Lewy Body Dementia patients. Consistent with reports of anti-parallel β-strands being a defining feature of toxic αS pre-fibrillar intermediates, I1 impacts the viability of neuroblasts and disrupts cell membranes, resulting in increased calcium influx. Our results integrate the occurrence of anti-parallel β-strands as salient features of toxic oligomers with their significant role in the amyloid fibril assembly pathway. These structural insights have implications for the development of therapies and biomarkers.
Non-Markovian Feedback for Optimized Quantum Error Correction
Matteo Puviani,
Sangkha Borah,
Remmy Augusta Menzata Zen,
Jan Ollé Aguilera,
Florian Marquardt
Physical Review Letters
134
020601
(2025)
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Bosonic codes allow the encoding of a logical qubit in a single component device, utilizing the infinitely large Hilbert space of a harmonic oscillator. In particular, the Gottesman-Kitaev-Preskill code has recently been demonstrated to be correctable well beyond the break-even point of the best passive encoding in the same system. Current approaches to quantum error correction (QEC) for this system are based on protocols that use feedback, but the response is based only on the latest measurement outcome. In our work, we use the recently proposed feedback-GRAPE (gradient-ascent pulse engineering with feedback) method to train a recurrent neural network that provides a QEC scheme based on memory, responding in a non-Markovian way to the full history of previous measurement outcomes, optimizing all subsequent unitary operations. This approach significantly outperforms current strategies and paves the way for more powerful measurement-based QEC protocols.
Neural-network-supported basis optimizer for the configuration interaction problem in quantum many-body clusters: Feasibility study and numerical proof
Pavlo Bilous,
Louis Thirion,
Henri Menke,
Maurits W. Haverkort,
Adriana Pálffy,
Philipp Hansmann
Physical Review B
111
035124
(2025)
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| PDF
A neural-network approach to optimize the selection of Slater determinants in configuration interaction for correlated electron systems is presented. We apply our algorithm to the selection of determinants in the discrete version of the single-impurity Anderson model, scaling up to large systems with as many as 299 determinants in a basis with 299 bath sites. By employing a neural network classifier and active learning, our approach significantly enhances computational efficiency by iteratively refining the selection of the most relevant determinants. We compare our method against a conventional basis truncation scheme without machine learning and demonstrate that our algorithm achieves a more compact and computationally efficient determinant selection while maintaining high accuracy. Given its straightforward applicability, our method offers a promising advancement for selective configuration interaction calculations in the study of correlated condensed matter systems.
Dynamic forces shape the survival fate of eliminated cells
Lakshmi Balasubramaniam,
Siavash Monfared,
Aleksandra Ardaševa,
Carine Rosse,
Andreas Schoenit,
Tien Dang,
Chrystelle Maric,
Mathieu Hautefeuille,
Leyla Kocgozlu, et al.
Tissues eliminate unfit, unwanted or unnecessary cells through cell extrusion, and this can lead to the elimination of both apoptotic and live cells. However, the mechanical signatures that influence the fate of extruding cells remain unknown. Here we show that modified force transmission across adherens junctions inhibits apoptotic cell eliminations. By combining cell experiments with varying levels of E-cadherin junctions and three-dimensional modelling of cell monolayers, we find that these changes not only affect the fate of the extruded cells but also shift extrusion from the apical to the basal side, leading to cell invasion into soft collagen gels. We generalize our findings using xenografts and cysts cultured in matrigel, derived from patients with breast cancer. Our results link intercellular force transmission regulated by cell–cell communication to cell extrusion mechanisms, with potential implications during morphogenesis and invasion of cancer cells.
A framework for the simulation of individual glycan coordinates to analyze spatial relationships within the glycocalyx
Sarah Fritsche,
Leonhard Möckl
Frontiers in Cell and Developmental Biology
12
(2025)
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The glycocalyx is a dense and dynamic layer of glycosylated species that covers every cell in the human body. It plays crucial roles in various cellular processes in health and disease, such as cancer immune evasion, cancer immune therapy, blastocyst implantation, and functional attenuation of membrane protein diffusion. In addition, alterations in glycocalyx structure may play an important role in ocular surface diseases, e.g., dry eye disease. Despite the emerging importance of the glycocalyx, various aspects of its functional organization remain elusive to date. A central reason for this elusiveness is the nanoscale dimension of the glycocalyx in conjunction with its high structural complexity, which is not accessible to observation with conventional light microscopy. Recent advances in super-resolution microscopy have enabled resolutions down to the single-digit nanometer range. In order to fully leverage the potential of these novel methods, computational frameworks that allow for contextualization of the resulting experimental data are required. Here, we present a simulation-based approach to analyze spatial relationships of glycan components on the cell membrane based on known geometrical parameters. We focus on sialic acids in this work, but the technique can be adapted to any glycan component of interest. By integrating data from mass spectrometry and quantitative biological studies, these simulations aim to model possible experimental outcomes, which can then be used for further analysis, such as spatial point statistics. Importantly, we include various experimental considerations, such as labeling and detection efficiency. This approach may contribute to establishing a new standard of connection between geometrical and molecular-resolution data in service of advancing our understanding of the functional role of the glycocalyx in biology as well as its clinical potential.
Quantum Science — a wonderful journey, ultimately empowering Technology
Cytoskeleton-functionalized synthetic cells with life-like mechanical features and regulated membrane dynamicity
Sebastian Novosedlik,
Felix Reichel,
Thijs van Veldenhuisen,
Yudong Li,
Hanglong Wu,
Henk Janssen,
Jochen Guck,
Jan van Hest
Nature Chemistry
17
356-364
(2025)
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The cytoskeleton is a crucial determinant of mammalian cell structure and function, providing mechanical resilience, supporting the cell membrane and orchestrating essential processes such as cell division and motility. Because of its fundamental role in living cells, developing a reconstituted or artificial cytoskeleton is of major interest. Here we present an approach to construct an artificial cytoskeleton that imparts mechanical support and regulates membrane dynamics. Our system involves amylose-based coacervates stabilized by a terpolymer membrane, with a cytoskeleton formed from polydiacetylene fibrils. The fibrils bundle due to interactions with the positively charged amylose derivative, forming micrometre-sized structures mimicking a cytoskeleton. Given the intricate interplay between cellular structure and function, the design and integration of this artificial cytoskeleton represent a crucial advancement, paving the way for the development of artificial cell platforms exhibiting enhanced life-like behaviour.
Thermally Assisted Microfluidics to Produce Chemically Equivalent Microgels with Tunable Network Morphologies
Dirk Rommel,
Bernhard Häßel,
Philip Pietryszek,
Matthias Mork,
Oliver Jung,
Meike Emondts,
Nikita Norkin,
Iris Christine Doolaar,
Yonka Kittel, et al.
Angewandte Chemie, International Edition in English
64
(1)
(2025)
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Although micron-sized microgels have become important building blocks in regenerative materials, offering decisive interactions with living matter, their chemical composition mostly significantly varies when their network morphology is tuned. Since cell behavior is simultaneously affected by the physical, chemical, and structural properties of the gel network, microgels with variable morphology but chemical equivalence are of interest. This work describes a new method to produce thermoresponsive microgels with defined mechanical properties, surface morphologies, and volume phase transition temperatures. A wide variety of microgels is synthesized by crosslinking monomers or star polymers at different temperatures using thermally assisted microfluidics. The diversification of microgels with different network structures and morphologies but of chemical equivalence offers a new platform of microgel building blocks with the ability to undergo phase transition at physiological temperatures. The method holds high potential to create soft and dynamic materials while maintaining the chemical composition for a wide variety of applications in biomedicine.
Preparing Schrödinger cat states in a microwave cavity using a neural network
Hector Hutin,
Pavlo Bilous,
Chengzhi Ye,
Sepideh Abdollahi,
Loris Cros,
Tom Dvir,
Tirth Shah,
Yonatan Cohen,
Audrey Bienfait, et al.
Scaling up quantum computing devices requires solving ever more complex quantum control tasks. Machine learning has been proposed as a promising approach to tackle the resulting challenges. However, experimental implementations are still scarce. In this work, we demonstrate experimentally a neural-network-based preparation of Schrödinger cat states in a cavity coupled dispersively to a qubit. We show that it is possible to teach a neural network to output optimized control pulses for a whole family of quantum states. After being trained in simulations, the network takes a description of the target quantum state as input and rapidly produces the pulse shape for the experiment, without any need for time-consuming additional optimization or retraining for different states. Our experimental results demonstrate more generally how deep neural networks and transfer learning can produce efficient simultaneous solutions to a range of quantum control tasks, which will benefit not only state preparation but also parametrized quantum gates.
How intercellular forces regulate cell competition
Andreas Schoenit,
Siavash Monfared,
Lucas Anger,
Carine Rosse,
Varun Venkatesh,
Lakshmi Balasubramaniam,
Elisabetta Marangoni,
Philippe Chavrier,
René‐Marc Mège, et al.
