Noise tolerance via reinforcement: learning a reinforced quantum dynamics

Journal of Statistical Mechanics Theory and Experiment, vol. 2025, pp. 123401

Abstract

Abstract The performance of quantum simulations heavily depends on the efficiency of noise mitigation techniques and error correction algorithms. Reinforcement has emerged as a powerful strategy to enhance the efficiency of learning and optimization algorithms. In this study, we demonstrate that a reinforced quantum dynamics can exhibit significant robustness against interactions with a noisy environment. We study a quantum annealing process in which reinforcement encourages the system to maintain its current state or follow a noise-free evolution. A learning algorithm is used to derive a concise approximation of these reinforced dynamics, thereby reducing the total evolution time and, consequently, the system’s exposure to noisy interactions. This also avoids the complexities associated with implementing quantum feedback in such reinforcement algorithms. The efficacy of our method is demonstrated through numerical simulations of reinforced quantum annealing with one-qubit and two-qubit systems under Pauli noise.

Authors 1

1. Abolfazl Ramezanpour corresponding Aachen Leiden Academic Centre for Drug Research Faculty of Mathematics and Natural Sciences

   Leiden University · Shiraz University

   Affiliation as printed

   Department of Physics, College of Science, Shiraz University, Shiraz 71454, Iran

   Leiden Academic Centre for Drug Research, Faculty of Mathematics and Natural Sciences, Leiden University, PO Box 9500-2300 RA, Leiden, The Netherlands