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Deep-learning approach for large atomic structure calculations

Pavlo Bilous, Adriana Pálffy, Florian Marquardt

High-precision atomic structure calculations require accurate modelling of electronic correlations involving large multiconfiguration wave function expansions. Here we develop a deep-learning approach which allows to preselect the most relevant configurations out of large basis sets until the targeted precision is achieved. Our method replaces a large multiconfiguration Dirac-Hartree-Fock computation by a series of smaller ones performed on an iteratively expanding basis subset managed by a convolutional neural network. The results for several examples with many-electron atoms show that deep learning can significantly reduce the required computational memory and running time and renders possible large-scale computations on otherwise unaccessible basis sets.

Tunneling-induced fractal transmission in Valley Hall waveguides

Tirth Shah, Florian Marquardt, Vittorio Peano

The Valley Hall effect provides a popular route to engineer robust waveguides for bosonic excitations such a photons and phonons. The almost complete absence of backscattering in many experiments has its theoretical underpinning in a smooth-envelope approximation that neglects large momentum transfer and is accurate only for small bulk band gaps and/or smooth domain walls. For larger bulk band gaps and hard domain walls backscattering is expected to become significant. Here, we show that in this experimentally relevant regime, the reflection of a wave at a sharp corner becomes highly sensitive on the orientation of the outgoing waveguide relative to the underlying lattice. Enhanced backscattering can be understood as being triggered by resonant tunneling transitions in quasimomentum space. Tracking the resonant tunneling energies as a function of the waveguide orientation reveals a self-repeating fractal pattern that is also imprinted in the density of states and the backscattering rate at a sharp corner.

Artificial Intelligence and Machine Learning for Quantum Technologies

Mario Krenn, Jonas Landgraf, Thomas Fösel, Florian Marquardt

In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade.