Machine Learning Methods as Robust Quantum Noise Estimators

TL;DR

This paper explores how machine learning models can be used to estimate and predict quantum noise in quantum circuits, aiding in the development of more reliable quantum computing systems.

Contribution

It demonstrates the effectiveness of ML models trained on random circuits to estimate quantum noise and assess circuit robustness across different IBM noise models.

Findings

ML models accurately predict quantum noise levels

Approach generalizes across multiple IBM noise models

Provides metrics for circuit stability and quality

Abstract

Access to quantum computing is steadily increasing each year as the speed advantage of quantum computers solidifies with the growing number of usable qubits. However, the inherent noise encountered when running these systems can lead to measurement inaccuracies, especially pronounced when dealing with large or complex circuits. Achieving a balance between the complexity of circuits and the desired degree of output accuracy is a nontrivial yet necessary task for the creation of production-ready quantum software. In this study, we demonstrate how traditional machine learning (ML) models can estimate quantum noise by analyzing circuit composition. To accomplish this, we train multiple ML models on random quantum circuits, aiming to learn to estimate the discrepancy between ideal and noisy circuit outputs. By employing various noise models from distinct IBM systems, our results illustrate how this approach can accurately predict the robustness of circuits with a low error rate. By providing metrics on the stability of circuits, these techniques can be used to assess the quality and security of quantum code, leading to more reliable quantum products.