Noise tolerance via reinforcement: Learning a reinforced quantum dynamics
This work addresses noise mitigation in quantum simulations, which is crucial for improving the efficiency of quantum computing, but it is incremental as it builds on existing reinforcement strategies.
The study tackled the problem of noise in quantum simulations by developing a reinforced quantum dynamics method, which demonstrated significant robustness against noisy environments and reduced evolution time by learning a concise approximation, as shown in numerical simulations with one- and two-qubit systems under Pauli noise.
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.