Extreme Quantum Advantage for Rare-Event Sampling

TL;DR

This paper presents a quantum algorithm that significantly outperforms classical methods in sampling rare events from complex stochastic processes, especially in spin systems, by reducing memory requirements exponentially.

Contribution

The paper introduces a novel quantum algorithm that achieves extreme memory advantages over classical algorithms for biased sampling of rare events in stochastic processes.

Findings

Quantum memory advantage ranges from polynomial to exponential.

Quantum advantage diverges when sampling rare equilibrium configurations.

Quantum algorithm outperforms classical biased sampling in efficiency.

Abstract

We introduce a quantum algorithm for efficient biased sampling of the rare events generated by classical memoryful stochastic processes. We show that this quantum algorithm gives an extreme advantage over known classical biased sampling algorithms in terms of the memory resources required. The quantum memory advantage ranges from polynomial to exponential and when sampling the rare equilibrium configurations of spin systems the quantum advantage diverges.