5.7k citations · 20.9k across the 136 of their papers we have counts for
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Deep Bayesian Experimental Design for Quantum Many-Body Systems
Leopoldo Sarra, Florian Marquardt
Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent dev…
Discovering Quantum Circuit Components with Program Synthesis
Leopoldo Sarra, Kevin Ellis, Florian Marquardt
Despite rapid progress in the field, it is still challenging to discover new ways to take advantage of quantum computation: all quantum algorithms need to be designed by hand, and…
Deep learning of spatial densities in inhomogeneous correlated quantum systems
Alex Blania, Sandro Herbig, Fabian Dechent +2
Machine learning has made important headway in helping to improve the treatment of quantum many-body systems. A domain of particular relevance are correlated inhomogeneous systems.…
Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system
Kevin Reuer, Jonas Landgraf, Thomas Fösel +10
To realize the full potential of quantum technologies, finding good strategies to control quantum information processing devices in real time becomes increasingly important. Usuall…
Learning Quantum Systems
Valentin Gebhart, Raffaele Santagati, Antonio Andrea Gentile +7
The future development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation and se…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, 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 learn…