Frontiers at the Intersection of Quantum Simulation and Machine Learning

The past two decades have brought considerable progress in two future key technologies – machine learning (ML) and quantum technology. The field of ML has seen various breakthroughs, such as the beating of the best human players in the game Go or unprecedented accuracy in the prediction of protein folding and image recognition, and sparks of artificial general intelligence; simultaneously, the first proof of principle applications in quantum chemistry and pioneering experiments for quantum simulation of correlated matter delineate new routes for scientific discovery using quantum technology.

Both fields are becoming increasingly intertwined in recent  attempts to provide insights into key challenges in quantum many-body physics. An objective of developing machine learning-enhanced numerical methods and quantum simulation is to push our capabilities to study exotic quantum phases or non-equilibrium states. Furthermore, while quantum simulators emerge as a new tool to address these outstanding research topics, their advent raises further questions that ML approaches can potentially solve. Prime use cases include the characterization and efficient experimental preparation of quantum many-body states.

This conference aims to bring together experts at the intersection of quantum simulation and ML, where quantum challenges match the natural strengths of ML and, reversely, the quantum applications call for developing new ML techniques.

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