Accelerating Density Matrix Renormalization Group Computations with Machine Learning

ORAL

Abstract

Density Matrix Renormalization Group (DMRG) has achieved great success as a technique for simulating one-dimensional and quasi-two-dimensional quantum systems. One major bottleneck for these computations is the variational procedure for ground state approximation. We investigate the application of machine learning methods to this problem and improve convergence time for various classes of strongly correlated systems.

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Presenters

  • Jacob Marks

    • Physics, Stanford University

Authors

  • Jacob Marks

    • Physics, Stanford University

  • Hong-Chen Jiang

    • Stanford Institute for Materials and Energy Sciences, SLAC and Stanford University
    • SIMES, SLAC, and Stanford University
    • SLAC National Accelerator Laboratory
    • Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory and Stanford University

  • Thomas Devereaux

    • Stanford University
    • Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory
    • SLAC National Accelerator Laboratory
    • Physics, Stanford University
    • SLAC and Stanford University
    • Institute for Materials and Energy Science, Stanford
    • SIMES, SLAC National Accelerator Lab
    • SLAC National Accelerator Laboratory and Stanford University, Stanford Institute for Materials and Energy Sciences
    • SLAC, Stanford
    • SIMES, SLAC, and Stanford University
    • Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory and Stanford University