Robust inference of memory structure for efficient quantum modelling of stochastic processes

TL;DR

This paper introduces a noise-robust, data-driven method for inferring the memory structure of quantum models of stochastic processes, enabling more efficient quantum modeling without prior classical knowledge.

Contribution

It presents a novel protocol for blind inference of quantum memory structures directly from data, overcoming limitations of classical inference methods.

Findings

Quantum models retain less information about the past than classical models.

The proposed method is robust to noise in the data.

It enables construction of efficient quantum models without prior classical model knowledge.

Abstract

A growing body of work has established the modelling of stochastic processes as a promising area of application for quantum techologies; it has been shown that quantum models are able to replicate the future statistics of a stochastic process whilst retaining less information about the past than any classical model must -- even for a purely classical process. Such memory-efficient models open a potential future route to study complex systems in greater detail than ever before, and suggest profound consequences for our notions of structure in their dynamics. Yet, to date methods for constructing these quantum models are based on having a prior knowledge of the optimal classical model. Here, we introduce a protocol for blind inference of the memory structure of quantum models -- tailored to take advantage of quantum features -- direct from time-series data, in the process highlighting the robustness of their structure to noise. This in turn provides a way to construct memory-efficient quantum models of stochastic processes whilst circumventing certain drawbacks that manifest solely as a result of classical information processing in classical inference protocols.