Predicting sampling advantage of stochastic Ising machines for quantum simulations
Phys. Rev. Applied 25, 024085 – Published 26 February, 2026
DOI: https://doi.org/10.1103/xl6n-dlq6
Abstract
This paper is a contribution to the Physical Review Applied collection titled Physics-Inspired Computing. Stochastic Ising machines, SIMs, are highly promising accelerators for optimization and sampling of computational problems that can be formulated as an Ising model. Here we investigate the computational advantage of SIM for simulations of quantum magnets with neural-network quantum states (NQS), in which the quantum many-body wave function is mapped onto an Ising model. We study the sampling performance of SIM for NQS by comparing sampling on a software-emulated SIM with standard Metropolis-Hastings sampling for NQS. We quantify the sampling efficiency by the number of computational steps required to reach isoaccurate stochastic estimation of the variational energy and show that this is entirely determined by the autocorrelation time of the sampling. This enables predictions of sampling advantage without direct deployment on hardware. Although sampling of the quantum Heisenberg models studied exhibits much longer autocorrelation times on SIMs, the massively parallel sampling of hardware SIMs leads to a projected speedup of 100 to 10 000, suggesting great opportunities for studying complex quantum systems at larger scales.
Physics Subject Headings (PhySH)
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This article appears in the following collection:
Collection on Physics-Inspired Computing
Physical Review Applied is pleased to present a Collection on Physics-Inspired Computing, highlighting the rapidly evolving field of energy-efficient computing techniques, from hardware technologies to algorithms, where physics inspiration serves as the crucial link. Contributions to this collection will be published throughout 2025. This Collection is being curated by Guest Editors Kerem Camsari and Supriyo Datta.
