mlsanityData to 7 Oct 2026

Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation

arXiv 1810.07207 ↗First posted 16 Oct 2018quant-ph cs.AI cs.LGcited by 27

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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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Oct 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 2; 2023: 7; 2024: 3; 2025: 6; 2026: 5.

27 papers.

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  1. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  2. 2023Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Networkcited by 40
  3. 2021Quantum Architecture Search via Continual Reinforcement Learningcited by 22
  4. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  5. 2023Deep Quantum Error Correctioncited by 10
  6. 2025Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correctioncited by 10
  7. 2023Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformerscited by 9
  8. 2025Artificial intelligence for representing and characterizing quantum systemscited by 9
  9. 2021A scalable and fast artificial neural network syndrome decoder for surface codescited by 8
  10. 2021Identifying optimal cycles in quantum thermal machines with reinforcement-learningcited by 6
  11. 2023Optimizing ZX-Diagrams with Deep Reinforcement Learningcited by 6
  12. 2025Quantum computing and artificial intelligence: status and perspectivescited by 5
15 more
  1. 2023Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learningcited by 4
  2. 2024Trainability issues in quantum policy gradientscited by 4
  3. 2025Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learningcited by 4
  4. 2023Machine-learning based noise characterization and correction on neutral atoms NISQ devicescited by 3
  5. 2025Scalable Neural Decoders for Practical Real-Time Quantum Error Correctioncited by 2
  6. 2022Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learningcited by 1
  7. 2022Decoding surface codes with deep reinforcement learning and probabilistic policy reusecited by 1
  8. 2024Enhancing Quantum Memory Lifetime with Measurement-Free Local Error Correction and Reinforcement Learningcited by 1
  9. 2024Artificially intelligent Maxwell's demon for optimal control of open quantum systemscited by 1
  10. 2026Replay-buffer engineering for noise-aware quantum circuit optimizationcited by 1
  11. 2026Maximum Likelihood Decoding of Quantum Error Correction Codescited by 1
  12. 2025SAQ: Stabilizer-Aware Quantum Error Correction Decodercited by 0
  13. 2026Neural Minimum Weight Perfect Matching for Quantum Error Codescited by 0
  14. 2026GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigationcited by 0
  15. 2026Machine Learning Approaches to Decoding Topological Quantum Codescited by 0