Machine Learning for Quantum Simulation: Virtual Conference
- Organized by
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Giuseppe Carleo, Ph.D.Assistant Professor, École Polytechnique
Research Scientist, CCQ (2018-2020), Flatiron Institute -
Andrew Millis, Ph.D.CCQ Co-Director, Flatiron Institute -
David Ceperley, Ph.D.Professor, Physics, University of Illinois at Urbana-Champaign
This virtual conference brings together the fast-growing community of researchers from condensed matter physics, quantum computing, quantum chemistry, and computer science working on the development of machine learning methods to study challenging problems in many-body quantum science.
Topics will include: Neural-network quantum states; Dynamics of many-body quantum systems; Tomography and characterization of quantum machines; Variational Algorithms on Quantum Hardware; Stochastic Optimization in Quantum Monte Carlo methods; ML-enhanced Density Functional Theory. The conference will also feature a session introducing the open-source NetKet software and selected applications.
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Ryan Babbush Google Federico Becca University of Trieste George Booth King's College London Kieron Burke University of California, Irvine Roberto Car Princeton University Juan Felipe Carrasquilla Vector Institute Kenny Choo University of Zurich Bryan Clark University of Illinois at Urbana-Champaign Weinan E Princeton University Sophia Economou Virginia Tech Markus Heyl Max-Planck-Institute for the Physics of Complex Systems Markus Holzmann Laboratoire de Physique Théorique de la Matière Condensée Roger Melko University of Waterloo Antonio Mezzacapo IBM Johan Mentink Radbound University Frank Noe Freie Universitaet Berlin Marivi Fernandez Serra Stony Brook University Sandro Sorella Scuola Internazionale Superiore di Studi Avanzati James Spencer DeepMind Frank Verstraete Ghent University Nobuyuki Yoshioka RIKEN Pan Zhang Chinese Academy of Sciences -
Monday, June 22
*All times are in EDT
9:20 - 9:30 AM Welcome message 9:30 - 10:05 AM (25+10) James Spencer (DeepMind) Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Network 10:05 - 10:40 AM (25+10) Roberto Car (Princeton University) Molecular simulation with the deep potential method 10:40 - 11:10 AM Break 11:10 - 11:45 AM (25+10) Frank Noé (Freie Universitaet Berlin) PauliNet - deep learning the electronic Schrödinger equation 11:45 - 12:20 PM (25+10) Kieron Burke (University of California, Irvine) Adventures in Machine Learning of density functionals 12:20 - 2:00 PM Break 2:00 - 2:35 PM (25+10) Marivi Fernandez Serra (Stony Brook University) Machine learning accurate exchange and correlation functionals of the electronic density 2:35 - 3:10 PM (25+10) Markus Holzmann (Laboratoire de Physique Théorique de la Matière Condensée) Iterative backflow renormalization: a non-linear network for quantum many-body wave functions in continuous space 3:10 - 3:45 PM (25+10) Weinan E (Princeton University) Developing meta-universal generalized density functional theory models and solving ground state Schrodinger equation for many-electron systems Tuesday, June 23
*All times are in EDT
9:30 - 10:05 AM (25+10) Sandro Sorella (Scuola Internazionale Superiore di Studi Avanzati) Unified approach for Variational and Auxiliary Field quantum Monte Carlo 10:05 - 10:40 AM (25+10) Kenny Choo (University of Zurich) Frustrated magnets and fermions with neural network quantum states 10:40 - 11:10 AM Break 11:10 - 11:45 AM (25+10) Bryan Clark (University of Illinois – Urbana Champaign) VMC in the era of ML: From wave-functions for Fermions to supervised wave-function optimization 11:45 - 12:20 PM (25+10) George Booth (King's College London) Gaussian Process States: Explicitly Data-driven wave functions 12:20 - 2:00 PM Break 2:00 - 2:15 PM (15) Giacomo Torlai (Flatiron Institute) Quantum process tomography with unsupervised learning and tensor networks 2:15 - 2:30 PM (15) Robert Huang (CalTech) Predicting Many Properties of a Quantum System from Very Few Measurements 2:30 - 2:35 PM Q & A 2:35 - 2:50 PM (15) Attila Szabó (Cambridge University) Neural network wave functions and the sign problem 2:50 - 3:05 PM (15) Christian Mendl (Technische Universität München) Real time evolution with neural-network quantum states by approximating the implicit midpoint method 3:05 - 3:10 PM Q & A 3:10 - 3:25 PM (15) James Stokes (Flatiron Institute) Quantum Information Geometry 3:25 - 3:40 PM (15) Yusuke Nomura (RIKEN) Application of machine learning beyond benchmarks reveals Dirac-type nodal spin liquid in the J1-J2 Heisenberg model 3:40 - 3:45 PM Q & A Monday, June 29
*All times are in EDT
9:30 - 10:05 AM (25+10) Juan Felipe Carrasquilla (Vector Institute) Neural autoregressive toolbox for many-body physics 10:05 - 10:40 AM (25+10) Frank Verstraete (Ghent University) From Boltzmann Machines to Tensor networks for simulating quantum many body systems 10:40 - 11:10 AM Break 11:10 - 11:45 AM (25+10) Federico Becca (University of Trieste) Low-energy spectrum of frustrated spin models (from a human-learning process) 11:45 - 12:20 PM (25+10) Roger Melko (University of Waterloo) Machine Learning for Quantum Error Correction 12:20 - 2:00 PM Break 2:00 - 2:35 PM (25+10) Sophia Economou (Virginia Tech) Toward efficient variational quantum algorithms 2:35 - 3:10 PM (25+10) Ryan Babbush (Google) Quantum Chemistry on the Google Sycamore Quantum Processor 3:10 - 3:45 PM (25+10) Antonio Mezzacapo (IBM) Neural-network estimators and locally-biased classical shadows for Quantum Chemistry Tuesday, June 30
*All times are in EDT
9:30 - 10:05 AM (25+10) Nobuyuki Yoshioka (RIKEN) Solving dissipative many-body system by neural quantum states 10:05 - 10:40 AM (25+10) Pan Zhang (Institute of Theoretical Physics, Chinese Academy of Sciences) Solving Statistical Mechanics: From Mean Field to Neural Networks, then to Tensor Networks 10:40 - 11:10 AM Break 11:10 - 11:45 AM (25+10) Markus Heyl (Max-Planck-Institute for the Physics of Complex Systems) Quantum many-body dynamics in two dimensions with artificial neural networks 11:45 - 12:20 PM (25+10) Johan Mentink (Radboud University) Simulating ultrafast dynamics in two-dimensional Heisenberg antiferromagnets with artificial neural networks 12:20 - 2:00 PM Break 2:00 - 2:15 PM (15) Fabien Alet (Laboratory of Theoretical Physics, IRSAMC) Combining RBM and Reptation QMC: application to a 2d bosonic model 2:15 - 2:30 PM (15) Marta Mauri (Zapata Computing) Neural Nets for Quantum Spin Systems Kets 2:30 - 2:35 PM Q & A 2:35 - 2:50 PM (15) Tom Westerhout (Radboud University) Wavefunction sign structure generalization in frustrated magnets 2:50 - 3:05 PM (15) ShengHsuan Lin (Technical University Munich) Symmetries with Neural Autoregressive Quantum States 3:05 - 3:10 PM Q & A 3:10 - 3:25 PM (15) Filippo Vincentini (Flatiron Institute) Variational Neural Network Ansätze for Open Quantum Systems 3:25 - 3:45 PM Damian Hofmann (Max Planck Institute for the Structure and Dynamics of Matter) Unitary quantum dynamics in driven spin systems with neural-network quantum states