Quantum noise modeling through Reinforcement Learning

TL;DR

This paper presents a reinforcement learning-based method to model and emulate quantum noise, improving flexibility over traditional techniques and validated on real superconducting qubits for quantum algorithm analysis.

Contribution

It introduces a novel reinforcement learning approach for quantum noise modeling, surpassing conventional methods in flexibility and accuracy.

Findings

RL agent effectively reproduces various noise models

Validated on real superconducting qubits

Enables practical quantum algorithm studies

Abstract

In the current era of quantum computing, robust and efficient tools are essential to bridge the gap between simulations and quantum hardware execution. In this work, we introduce a machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. Our algorithm leverages reinforcement learning, offering increased flexibility in reproducing various noise models compared to conventional techniques such as randomized benchmarking or heuristic noise models. The effectiveness of the RL agent has been validated through simulations and testing on real superconducting qubits. Additionally, we provide practical use-case examples for the study of renowned quantum algorithms.