Sampling scheme for neuromorphic simulation of entangled quantum systems

• Published in: arXiv.org
• Main Authors: Czischek, Stefanie, Pawlowski, Jan M, Gasenzer, Thomas, Gärttner, Martin
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 03.12.2019
• Subjects: Complexity; Computation; Computer simulation; Entangled states; Hardware; Neural networks; Physics - Disordered Systems and Neural Networks; Physics - Quantum Physics; Sampling
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1907.12844

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 which 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.