Machine Learning Methods as Robust Quantum Noise Estimators
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.
Jon Gardeazabal-Gutierrez Erik B. Terres-Escudero Pablo García Bringas