# Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation

arXiv: https://arxiv.org/abs/1810.07207 · HTML: https://mlsanity.com/paper/1810.07207/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 28 of 28)

- [Variational Quantum Reinforcement Learning via Evolutionary Optimization](https://mlsanity.com/paper/2109.00540/) (2021)
- [Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network](https://mlsanity.com/paper/2310.05900/) (2023)
- [Quantum Architecture Search via Continual Reinforcement Learning](https://mlsanity.com/paper/2112.05779/) (2021)
- [Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications](https://mlsanity.com/paper/2307.00908/) (2023)
- [Deep Quantum Error Correction](https://mlsanity.com/paper/2301.11930/) (2023)
- [Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correction](https://mlsanity.com/paper/2504.14557/) (2025)
- [Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers](https://mlsanity.com/paper/2311.16082/) (2023)
- [Artificial intelligence for representing and characterizing quantum systems](https://mlsanity.com/paper/2509.04923/) (2025)
- [A scalable and fast artificial neural network syndrome decoder for surface codes](https://mlsanity.com/paper/2110.05854/) (2021)
- [Identifying optimal cycles in quantum thermal machines with reinforcement-learning](https://mlsanity.com/paper/2108.13525/) (2021)
- [Optimizing ZX-Diagrams with Deep Reinforcement Learning](https://mlsanity.com/paper/2311.18588/) (2023)
- [Quantum computing and artificial intelligence: status and perspectives](https://mlsanity.com/paper/2505.23860/) (2025)
- [Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning](https://mlsanity.com/paper/2305.06378/) (2023)
- [Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning](https://mlsanity.com/paper/2502.14372/) (2025)
- [Trainability issues in quantum policy gradients](https://mlsanity.com/paper/2406.09614/) (2024)
- [Machine-learning based noise characterization and correction on neutral atoms NISQ devices](https://mlsanity.com/paper/2306.15628/) (2023)
- [Scalable Neural Decoders for Practical Real-Time Quantum Error Correction](https://mlsanity.com/paper/2510.22724/) (2025)
- [Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learning](https://mlsanity.com/paper/2204.04785/) (2022)
- [Decoding surface codes with deep reinforcement learning and probabilistic policy reuse](https://mlsanity.com/paper/2212.11890/) (2022)
- [Enhancing Quantum Memory Lifetime with Measurement-Free Local Error Correction and Reinforcement Learning](https://mlsanity.com/paper/2408.09524/) (2024)
- [Artificially intelligent Maxwell's demon for optimal control of open quantum systems](https://mlsanity.com/paper/2408.15328/) (2024)
- [Neural Minimum Weight Perfect Matching for Quantum Error Codes](https://mlsanity.com/paper/2601.00242/) (2026)
- [Replay-buffer engineering for noise-aware quantum circuit optimization](https://mlsanity.com/paper/2604.21863/) (2026)
- [Maximum Likelihood Decoding of Quantum Error Correction Codes](https://mlsanity.com/paper/2605.17230/) (2026)
- [Machine Learning Approaches to Decoding Topological Quantum Codes](https://mlsanity.com/paper/2608.15760/) (2026)
- [SAQ: Stabilizer-Aware Quantum Error Correction Decoder](https://mlsanity.com/paper/2512.08914/) (2025)
- [GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation](https://mlsanity.com/paper/2604.24551/) (2026)
- [Distance Prediction for Quantum Codes](https://mlsanity.com/paper/2610.18130/) (2026)

## Abstract (arXiv)

Topological error correcting codes, and particularly the surface code, currently provide the most feasible roadmap towards large-scale fault-tolerant quantum computation. As such, obtaining fast and flexible decoding algorithms for these codes, within the experimentally relevant context of faulty syndrome measurements, is of critical importance. In this work, we show that the problem of decoding such codes, in the full fault-tolerant setting, can be naturally reformulated as a process of repeated interactions between a decoding agent and a code environment, to which the machinery of reinforcement learning can be applied to obtain decoding agents. As a demonstration, by using deepQ learning, we obtain fast decoding agents for the surface code, for a variety of noise-models.