The MPL Theory Division, led by Florian Marquardt, deals with both the quantum and classical dynamics of systems relevant for modern optics research, especially at the interface between nanophysics and quantum optics. Topics include the theory of optomechanics, neural networks and machine learning for physics, quantum optics in superconducting circuits, transport in photonic systems, fundamental questions of quantum many-body theory, nonequilibrium nonlinear dynamics, and decoherence. The group applies a variety of approaches, ranging from analytical studies to numerical simulations.
Our independent theory research group led by Mario Krenn (founded in September 2021), investigates how new artificial intelligence (AI) can make conceptual advances in physics, particularly quantum physics and quantum optics. Read more here.
Our independent theory research group dedicated to non-Hermitian topological phenomena, led by Flore Kunst, started in November 2021. Read more here.
We are part of the following research networks:
For all general inquiries, please contact us at:
Max Planck Institute for the Science of Light
D-91058 Erlangen, Germany
The Max Planck Institute is located right next to the Science Campus of the Friedrich-Alexander-University Erlangen-Nuremberg, on its northern edge. See the information page on how to find us.
In our research, we apply tools from condensed matter theory and from quantum optics to a range of questions at the interface of nanophysics and quantum optics, addressing both quantum and classical dynamics. In our approach, we often try to identify the salient features of experimentally relevant situations and condense them into minimalist models which can then be attacked with all the state-of-the-art theoretical tools. At the same time, we also care about the direct contact with experiments, down to designing the classical electromagnetic and acoustic properties of specific structures.
Deep Learning of Quantum Many-Body Dynamics via Random Driving
Naeimeh Mohseni, Thomas Fösel, Lingzhen Guo, Carlos Navarrete-Benlloch, Florian Marquardt
Neural networks have emerged as a powerful way to approach many practical problems in quantumphysics. In this work, we illustrate the power of deep learning to predict the dynamics of a quantummany-body system, where the training is based purely on monitoring expectation values of observables under random driving. The trained recurrent network is able to produce accurate predictions for driving trajectories entirely different than those observed during training. As a proof of principle, here we train the network on numerical data generated from spin models, showing that it can learn the dynamics of observables of interest without needing information about the full quantum state.This allows our approach to be applied eventually to actual experimental data generated from aquantum many-body system that might be open, noisy, or disordered, without any need for a detailedunderstanding of the system. This scheme provides considerable speedup for rapid explorations andpulse optimization. Remarkably, we show the network is able to extrapolate the dynamics to times longer than those it has been trained on, as well as to the infinite-system-size limit.
Ising machines: Hardware solvers for combinatorial optimization problems
Naeimeh Mohseni, Peter McMahon, Tim Byrnes
Ising machines are hardware solvers which aim to find the absolute or approximate ground states of the Ising model. The Ising model is of fundamental computational interest because it is possible to formulate any problem in the complexity class NP as an Ising problem with only polynomial overhead. A scalable Ising machine that outperforms existing standard digital computers could have a huge impact for practical applications for a wide variety of optimization problems. In this review, we survey the current status of various approaches to constructing Ising machines and explain their underlying operational principles. The types of Ising machines considered here include classical thermal annealers based on technologies such as spintronics, optics, memristors, and digital hardware accelerators; dynamical-systems solvers implemented with optics and electronics; and superconducting-circuit quantum annealers. We compare and contrast their performance using standard metrics such as the ground-state success probability and time-to-solution, give their scaling relations with problem size, and discuss their strengths and weaknesses.
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena, Alexander Gresch, Marcin Płodzień, Kaelan Donatella, Kim Nicoli, Paolo Stornati, Rouven Koch, et al.
In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.