Machine learning techniques are rapidly being adopted into the field of quantum many-body physics including condensed matter theory experiment and quantum information science. The steady increase in data being produced by highly-controlled quantum experiments brings the potential of machine learning algorithms to the forefront of scientific advancement. Particularly exciting is the prospect of using machine learning for the discovery and design of quantum materials devices and computers. In order to make progress the field must address a number of fundamental questions related to the challenges of studying many-body quantum mechanics using classical computing algorithms and hardware. The goal of this conference is to bring together experts in computational physics machine learning and quantum information to make headway on a number of related topics including: Data-drive quantum state reconstruction Machine learning strategies for quantum error correction Neural-network based wavefunctions Near-term prospects for data from quantum devices Machine learning for quantum algorithm discovery Registration for this event is now closed
Format results
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Differentiable Programming Tensor Networks and Quantum Circuits
Lei Wang Chinese Academy of Sciences
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RL-driven Quantum Computation
Pooya Ronagh Perimeter Institute for Theoretical Physics
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Glassy and Correlated Phases of Optimal Quantum Control
Marin Bukov University of California System
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Neural Belief-Propagation Decoders for Quantum Error-Correcting Codes
Yehua Liu University of Sherbrooke
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Operational quantum tomography
Olivia Di Matteo TRIUMF (Canada's National Laboratory for Particle and Nuclear Physics)
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Machine learning phase discovery in quantum gas microscope images
Ehsan Khatami San Jose State University
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Machine Learning Physics: From Quantum Mechanics to Holographic Geometry
Yi-Zhuang You University of California System
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Deep learning and density functional theory
Isaac Tamblyn National Research Council Canada (NRC)
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Machine learning ground-state energies and many-body wave function
Sebastiano Pilati University of Camerino
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