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CCQ
CCQ

Machine Learning for Quantum Simulation: Virtual Conference

  • Organized by
  • 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
Date & Time


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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  • Invited Speakersplus--large
    Ryan BabbushGoogle
    Federico BeccaUniversity of Trieste
    George Booth King's College London
    Kieron BurkeUniversity of California, Irvine
    Roberto CarPrinceton University
    Juan Felipe CarrasquillaVector Institute
    Kenny ChooUniversity of Zurich
    Bryan ClarkUniversity of Illinois at Urbana-Champaign
    Weinan EPrinceton University
    Sophia EconomouVirginia Tech
    Markus HeylMax-Planck-Institute for the Physics of Complex Systems
    Markus HolzmannLaboratoire de Physique Théorique de la Matière Condensée
    Roger MelkoUniversity of Waterloo
    Antonio MezzacapoIBM
    Johan MentinkRadbound University
    Frank NoeFreie Universitaet Berlin
    Marivi Fernandez Serra Stony Brook University
    Sandro SorellaScuola Internazionale Superiore di Studi Avanzati
    James SpencerDeepMind
    Frank VerstraeteGhent University
    Nobuyuki YoshiokaRIKEN
    Pan ZhangChinese Academy of Sciences
  • Agendaplus--large

    Monday, June 22

    *All times are in EDT

    9:20 - 9:30 AMWelcome 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 PMBreak
    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 PMBreak
    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 PMQ & 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 PMQ & 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 PMQ & 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 PMBreak
    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 PMBreak
    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 PMQ & 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 PMQ & 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

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