# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1907.12844