Machine Learning of Average Non-Markovianity from Randomized Benchmarking

TL;DR

This paper introduces a machine learning approach using matrix product operators to quantify the minimal average non-Markovianity in randomized benchmarking data, aiding the assessment of temporal correlations in quantum circuits.

Contribution

It presents a novel machine learning method to operationally quantify non-Markovianity from randomized benchmarking data, advancing the analysis of quantum noise correlations.

Findings

Successfully deduces minimal average non-Markovianity from RB data

Applicable to various gate sets and RB techniques

Enhances understanding of temporal correlations in quantum devices

Abstract

The presence of correlations in noisy quantum circuits will be an inevitable side effect as quantum devices continue to grow in size and depth. Randomized Benchmarking (RB) is arguably the simplest method to initially assess the overall performance of a quantum device, as well as to pinpoint the presence of temporal-correlations, so-called non-Markovianity; however, when such presence is detected, it hitherto remains a challenge to operationally quantify its features. Here, we demonstrate a method exploiting the power of machine learning with matrix product operators to deduce the minimal average non-Markovianity displayed by the data of a RB experiment, arguing that this can be achieved for any suitable gate set, as well as tailored for most specific-purpose RB techniques.