mlsanityData to 7 Oct 2026

Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network

arXiv 2310.05900 ↗First posted 9 Oct 2023quant-ph cs.LGcited by 40

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Abstract (arXiv)

Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google's Sycamore quantum processor for distance 3 and 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk, leakage, and analog readout signals, and sustains its accuracy far beyond the 25 cycles it was trained on. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 26 of its 133 references that are in the map. First posted on arXiv 9 Oct 2023.

Builds on

26 of 133 references.

References · 26

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  2. 2020Language Models are Few-Shot Learnerscited by 32,199
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  12. 2020Power of data in quantum machine learningcited by 336
14 more
  1. 2021Quantum advantage in learning from experimentscited by 186
  2. 2020Recurrent Quantum Neural Networkscited by 79
  3. 2018Reinforcement Learning Decoders for Fault-Tolerant Quantum Computationcited by 27
  4. 2018Deep neural decoders for near term fault-tolerant experimentscited by 14
  5. 2023Deep Quantum Error Correctioncited by 10
  6. 2018Advantages of versatile neural-network decoding for topological codescited by 9
  7. 2018Neural Network Decoders for Large-Distance 2D Toric Codescited by 8
  8. 2021A scalable and fast artificial neural network syndrome decoder for surface codescited by 8
  9. 2023qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformerscited by 8
  10. 2023The END: An Equivariant Neural Decoder for Quantum Error Correctioncited by 5
  11. 2020Learnability and Complexity of Quantum Samplescited by 4
  12. 2023A Scalable, Fast and Programmable Neural Decoder for Fault-Tolerant Quantum Computation Using Surface Codescited by 3
  13. 2023Scalable Quantum Error Correction for Surface Codes using FPGAcited by 2
  14. 2022Decoding surface codes with deep reinforcement learning and probabilistic policy reusecited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 17; 2026: 21.

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  1. 2025AlphaEvolve: A coding agent for scientific and algorithmic discoverycited by 763
  2. 2024Qiskit Code Assistant: Training LLMs for generating Quantum Computing Codecited by 24
  3. 2025A Survey of Continual Reinforcement Learningcited by 16
  4. 2025Artificial intelligence for representing and characterizing quantum systemscited by 9
  5. 2025A Primer on Quantum Machine Learningcited by 9
  6. 2025A scalable and real-time neural decoder for topological quantum codescited by 8
  7. 2025Efficient and Universal Neural-Network Decoder for Stabilizer-Based Quantum Error Correctioncited by 6
  8. 2025MetaSym: A Symplectic Meta-learning Framework for Physical Intelligencecited by 5
  9. 2025Quantum computing and artificial intelligence: status and perspectivescited by 5
  10. 2026Scalable Neural Decoders for Practical Fault-Tolerant Quantum Computationcited by 4
  11. 2026Rethink the Role of Neural Decoders in Quantum Error Correctioncited by 3
  12. 2025Scalable Neural Decoders for Practical Real-Time Quantum Error Correctioncited by 2
28 more
  1. 2026Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correctioncited by 2
  2. 2025Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correctioncited by 1
  3. 2025Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learningcited by 1
  4. 2026Universal computation is intrinsic to language model decodingcited by 1
  5. 2026Maximum Likelihood Decoding of Quantum Error Correction Codescited by 1
  6. 2026Efficient foundation decoders for fault-tolerant quantum computingcited by 1
  7. 2024Autoregressive model path dependence near Ising criticalitycited by 0
  8. 2025Noise tolerance via reinforcement: Learning a reinforced quantum dynamicscited by 0
  9. 2025Parity Cross-Resonance: A Multiqubit Gatecited by 0
  10. 2025Qubit-centric Transformer for Surface Code Decodingcited by 0
  11. 2025Data-Driven Learnability Transition of Measurement-Induced Entanglementcited by 0
  12. 2025SAQ: Stabilizer-Aware Quantum Error Correction Decodercited by 0
  13. 2025Evolutionary BP+OSD Decoding for Low-Latency Quantum Error Correctioncited by 0
  14. 2026Neural Minimum Weight Perfect Matching for Quantum Error Codescited by 0
  15. 2026A Mixture of Experts Vision Transformer for High-Fidelity Surface Code Decodingcited by 0
  16. 2026Trapped by simplicity: When Transformers fail to learn from noisy featurescited by 0
  17. 2026O(n) alternative to Quantum Fourier Transform with efficient neural net classical post-processingcited by 0
  18. 2026Sparse Mamba Decoder for Quantum Error Correction: Efficient Defect-Centric Processing of Surface Code Syndromescited by 0
  19. 2026Latency-Constrained Hardware-Aware Quantum Error Correction Co-Design with Adaptive Confidence-Gated Neural Decoding for the Rotated Surface Codecited by 0
  20. 2026MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recoverycited by 0
  21. 2026Qubit-Efficient Quantum Search for Hyperdimensional Decomposition via Logarithmic Encodingcited by 0
  22. 2026Machine-learned syndrome post-selection for reliable quantum error correctioncited by 0
  23. 2026QAdapt: A Noise-Adaptive Neural Pre-Decoding Framework for Quantum Error Correctioncited by 0
  24. 2026Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulationcited by 0
  25. 2026Machine Learning Approaches to Decoding Topological Quantum Codescited by 0
  26. 2026A Sim-to-Real Study of Surface-Code Decoder Benchmarkingcited by 0
  27. 2026Evaluating Verified Autonomy in Quantum Engineeringcited by 0
  28. 2026Noise-Robust Quantum State Characterization for Remote State Preparation with Deep Learningcited by 0