Classical Quantum Optimization with Neural Network Quantum States

Joseph Gomes; Keri A McKiernan; Peter Eastman; Vijay S Pande

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Classical Quantum Optimization with Neural Network Quantum States

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Classical Quantum Optimization with Neural Network Quantum States

Joseph Gomes, Keri A McKiernan, Peter Eastman and Vijay S Pande

ArXiv.org

10/23/2019

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https://arxiv.org/abs/1910.10675View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

The classical simulation of quantum systems typically requires exponential resources. Recently, the introduction of a machine learning-based wavefunction ansatz has led to the ability to solve the quantum many-body problem in regimes that had previously been intractable for existing exact numerical methods. Here, we demonstrate the utility of the variational representation of quantum states based on artificial neural networks for performing quantum optimization. We show empirically that this methodology achieves high approximation ratio solutions with polynomial classical computing resources for a range of instances of the Maximum Cut (MaxCut) problem whose solutions have been encoded into the ground state of quantum many-body systems up to and including 256 qubits.

Details

Title: Subtitle: Classical Quantum Optimization with Neural Network Quantum States
Creators: Joseph Gomes
Keri A McKiernan
Peter Eastman
Vijay S Pande
Resource Type: Preprint
Publication Details: ArXiv.org
ISSN: 2331-8422
Comment: Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019), Vancouver, Canada
Language: English
Date posted: 10/23/2019
Academic Unit: Chemical and Biochemical Engineering
Record Identifier: 9984209501702771

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