Sampling scheme for neuromorphic simulation of entangled quantum systems

• Verfasst von: Czischek, Stefanie [VerfasserIn]
• Verfasst von: Pawlowski, Jan M. [VerfasserIn]
• Verfasst von: Gasenzer, Thomas [VerfasserIn]
• Verfasst von: Gärttner, Martin [VerfasserIn]
• Titel: Sampling scheme for neuromorphic simulation of entangled quantum systems
• Verf.angabe: Stefanie Czischek, Jan M. Pawlowski, Thomas Gasenzer, and Martin Gärttner
• E-Jahr: 2019
• Jahr: 13 November 2019
• Umfang: 14 S.
• Fussnoten: Gesehen am 11.03.2020
• Titel Quelle: Enthalten in: Physical review
• Ort Quelle: Woodbury, NY : Inst., 2016
• Jahr Quelle: 2019
• Band/Heft Quelle: 100(2019,19) Artikel-Nummer 195120, 14 Seiten
• ISSN Quelle: 2469-9969
• DOI: doi:10.1103/PhysRevB.100.195120
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1692290495

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.