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Sampling scheme for neuromorphic simulation of entangled quantum systems

Stefanie Czischek, Jan M. Pawlowski, Thomas Gasenzer, and Martin Gärttner
Phys. Rev. B 100, 195120 – Published 13 November 2019

Abstract

Due to the complexity of the space of quantum many-body states, the computation of expectation values by statistical sampling is, in general, a hard task. Neural network representations of such quantum states, which can be physically implemented by neuromorphic hardware, could enable efficient sampling. A scheme is proposed that leverages this capability to speed up sampling from so-called neural quantum states encoded by a restricted Boltzmann machine. Due to the complex network parameters, a direct hardware implementation is not feasible. We overcome this problem by considering a phase-reweighting scheme for sampling expectation values of observables. Applying our method to a set of paradigmatic entangled quantum states we find that, in general, the phase-reweighted sampling is subject to a form of sign problem, which renders the sampling computationally costly. The use of neuromorphic chips could allow reducing computation times and thereby extend the range of tractable system sizes.

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  • Received 1 August 2019

DOI:https://doi.org/10.1103/PhysRevB.100.195120

©2019 American Physical Society

Physics Subject Headings (PhySH)

General PhysicsCondensed Matter, Materials & Applied PhysicsNetworks

Authors & Affiliations

Stefanie Czischek1, Jan M. Pawlowski2, Thomas Gasenzer1, and Martin Gärttner1

  • 1Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany
  • 2Institut für Theoretische Physik, Ruprecht-Karls-Universität Heidelberg, Philosophenweg 16, 69120 Heidelberg, Germany

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Issue

Vol. 100, Iss. 19 — 15 November 2019

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