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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.01252 |
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Table of Contents:
- Non-Markovian memory effects in open quantum systems provide valuable resources for preserving coherence and enhancing controllability. However, exploiting them requires strategies adapted to history-dependent dynamics. We introduce a reinforcement-learning framework that autonomously learns to amplify information backflow in a driven two-level system coupled to a structured reservoir. Using a reward based on the positive time derivative of the trace distance associated with the Breuer-Laine-Piilo measure, we train PPO and SAC agents and benchmark their performance against gradient-based optimal control theory (OCT). While OCT enhances a single dominant backflow peak, RL policies broaden this revival and activate additional contributions in later memory windows, producing sustained positive trace-distance growth over a longer duration. Consequently, the integrated non-Markovianity achieved by RL substantially exceeds that obtained with OCT. These results demonstrate how long-horizon, model-free learning naturally uncovers distributed-backflow strategies and highlight the potential of reinforcement learning for engineering memory effects in open quantum systems.