Noise tolerance via reinforcement: Learning a reinforced quantum dynamics

TL;DR

This paper introduces a reinforcement-based approach to enhance the robustness of quantum annealing against noise, reducing evolution time and avoiding complex feedback mechanisms, demonstrated through simulations with small quantum systems.

Contribution

It presents a novel reinforcement strategy for quantum dynamics that improves noise tolerance and simplifies implementation in quantum annealing.

Findings

Reinforced quantum annealing shows increased robustness against Pauli noise.

The method reduces total evolution time, decreasing noise exposure.

Numerical simulations validate the effectiveness for one- and two-qubit systems.

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 where, through reinforcement, the system is encouraged to maintain its current state or follow a noise-free evolution. A learning algorithm is employed to derive a concise approximation of this reinforced dynamics, 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- and two-qubit systems under Pauli noise.