# Noise tolerance via reinforcement: Learning a reinforced quantum dynamics

arXiv: https://arxiv.org/abs/2506.12418 · HTML: https://mlsanity.com/paper/2506.12418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 14 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network](https://mlsanity.com/paper/2310.05900/) (2023)
- [A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance](https://mlsanity.com/paper/2401.11351/) (2024)
- [Machine Learning for Practical Quantum Error Mitigation](https://mlsanity.com/paper/2309.17368/) (2023)
- [Optimization by a quantum reinforcement algorithm](https://mlsanity.com/paper/1706.04262/) (2017)
- [Finding a marked node on any graph by continuous-time quantum walk](https://mlsanity.com/paper/1807.05957/) (2018)
- [Quantum walk in a reinforced free-energy landscape: Quantum annealing with reinforcement](https://mlsanity.com/paper/2202.10908/) (2022)

## Abstract (arXiv)

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.