Sampling scheme for neuromorphic simulation of entangled quantum systems

TL;DR

This paper proposes a phase reweighting sampling scheme for neuromorphic hardware to efficiently simulate entangled quantum systems, addressing computational challenges and potential hardware advantages.

Contribution

It introduces a novel phase reweighting approach for neural quantum state sampling on neuromorphic hardware, enabling faster expectation value computation despite complex parameters.

Findings

Phase reweighting can mitigate sign problems in quantum state sampling.

Neuromorphic hardware may reduce computation times for entangled states.

Sign problem persists, affecting computational efficiency.

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 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.