Noise tolerance via reinforcement: learning a reinforced quantum dynamics
Year: 2025
Type: article
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.
Author: Abolfazl Ramezanpour
Institutions: Leiden University, Shiraz University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
OpenAlex ID: W4416852718