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Overcoming barriers to scalability in variational quantum Monte Carlo

Authors:

Shravan VeerapaneniAuthors Info & Claims

SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 62, Pages 1 - 13

https://doi.org/10.1145/3458817.3476219

Published: 13 November 2021 Publication History

Abstract

The variational quantum Monte Carlo (VQMC) method received significant attention in the recent past because of its ability to overcome the curse of dimensionality inherent in many-body quantum systems. Close parallels exist between VQMC and the emerging hybrid quantum-classical computational paradigm of variational quantum algorithms. VQMC overcomes the curse of dimensionality by performing alternating steps of Monte Carlo sampling from a parametrized quantum state followed by gradient-based optimization. While VQMC has been applied to solve high-dimensional problems, it is known to be difficult to parallelize, primarily owing to the Markov Chain Monte Carlo (MCMC) sampling step. In this work, we explore the scalability of VQMC when autoregressive models, with exact sampling, are used in place of MCMC. This approach can exploit distributed-memory, shared-memory and/or GPU parallelism in the sampling task without any bottlenecks. In particular, we demonstrate GPU-scalability of VQMC for solving up to ten-thousand dimensional combinatorial optimization problems.

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Cited By

Bortone MRath YBooth G(2024)Impact of conditional modelling for a universal autoregressive quantum stateQuantum10.22331/q-2024-02-08-12458(1245)Online publication date: 8-Feb-2024
https://doi.org/10.22331/q-2024-02-08-1245
Zhao TStokes JVeerapaneni S(2023)Scalable neural quantum states architecture for quantum chemistryMachine Learning: Science and Technology10.1088/2632-2153/acdb2f4:2(025034)Online publication date: 23-Jun-2023
https://doi.org/10.1088/2632-2153/acdb2f
Zhao TStokes JVeerapaneni S(2023)Meta-variational quantum Monte CarloQuantum Machine Intelligence10.1007/s42484-022-00094-w5:1Online publication date: 22-Jan-2023
https://doi.org/10.1007/s42484-022-00094-w
Show More Cited By

Index Terms

Overcoming barriers to scalability in variational quantum Monte Carlo
1. Applied computing
  1. Physical sciences and engineering
    1. Physics
2. Computing methodologies

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cover image ACM Conferences

SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2021

1493 pages

ISBN:9781450384421

DOI:10.1145/3458817

General Chair:
Bronis R. de Supinski,
Program Chairs:
Mary Hall,
Todd Gamblin

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 13 November 2021

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SC '21

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SC '21: The International Conference for High Performance Computing, Networking, Storage and Analysis

November 14 - 19, 2021

Missouri, St. Louis

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Cited By

Bortone MRath YBooth G(2024)Impact of conditional modelling for a universal autoregressive quantum stateQuantum10.22331/q-2024-02-08-12458(1245)Online publication date: 8-Feb-2024
https://doi.org/10.22331/q-2024-02-08-1245
Zhao TStokes JVeerapaneni S(2023)Scalable neural quantum states architecture for quantum chemistryMachine Learning: Science and Technology10.1088/2632-2153/acdb2f4:2(025034)Online publication date: 23-Jun-2023
https://doi.org/10.1088/2632-2153/acdb2f
Zhao TStokes JVeerapaneni S(2023)Meta-variational quantum Monte CarloQuantum Machine Intelligence10.1007/s42484-022-00094-w5:1Online publication date: 22-Jan-2023
https://doi.org/10.1007/s42484-022-00094-w
Vargas-Calderón VVinck-Posada HGonzález F(2023)An empirical study of quantum dynamics as a ground state problem with neural quantum statesQuantum Information Processing10.1007/s11128-023-03902-922:4Online publication date: 7-Apr-2023
https://doi.org/10.1007/s11128-023-03902-9
Li QWu HQian WLi XZhu QYang S(2022)Portfolio Optimization Based on Quantum HHL AlgorithmArtificial Intelligence and Security10.1007/978-3-031-06788-4_8(90-99)Online publication date: 15-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-06788-4_8

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