Learning Robust Observable to Address Noise in Quantum Machine Learning

TL;DR

This paper introduces a machine-learning framework to identify quantum observables that are inherently resistant to noise, aiming to improve the robustness and reliability of quantum machine learning models in noisy quantum environments.

Contribution

It proposes a novel approach for learning noise-robust observables in quantum systems, demonstrated through a toy example and multiple quantum circuits, enhancing QML stability.

Findings

Robust observables can be learned that outperform conventional ones under noisy conditions.

The framework successfully applies to multiple two-qubit circuits and noisy channels.

Robust observables improve the stability of quantum machine learning models.

Abstract

Quantum Machine Learning (QML) has emerged as a promising field that combines the power of quantum computing with the principles of machine learning. One of the significant challenges in QML is dealing with noise in quantum systems, especially in the Noisy Intermediate-Scale Quantum (NISQ) era. Noise in quantum systems can introduce errors in quantum computations and degrade the performance of quantum algorithms. In this paper, we propose a framework for learning observables that are robust against noisy channels in quantum systems. We demonstrate that it is possible to learn observables that remain invariant under the effects of noise and show that this can be achieved through a machine-learning approach. We present a toy example using a Bell state under a depolarization channel to illustrate the concept of robust observables. We then describe a machine-learning framework for learning such observables across six two-qubit quantum circuits and five noisy channels. Our results show that it is possible to learn observables that are more robust to noise than conventional observables. We discuss the implications of this finding for quantum machine learning, including potential applications in enhancing the stability of QML models in noisy environments. By developing techniques for learning robust observables, we can improve the performance and reliability of quantum machine learning models in the presence of noise, contributing to the advancement of practical QML applications in the NISQ era.