Description
An approximate qubit code obtained from a numerical optimization involving a reinforcement learning agent.Protection
Depends on the parameter being optimized.Rate
Neural network codes can be obtained by optimizing the coherent information [2].Encoding
Both codes and encoding circuits can be obtained via a reinforcement learning agent [4].Notes
See review on the use of artificial intelligence in quantum error correction [5].Cousins
- Kitaev surface code— Reinforcement learners can be used to optimize the geometry of the surface code to be more suited to a noise channel [3].
- Numerically optimized bosonic code— Numerically optimized bosonic codes can be obtained via reinforcement learning [6,7].
- Small-distance qubit stabilizer code— 13 inequivalent \([[9,3,3]]\) codes, along with others, have been found via reinforcement learning [4].
- Tensor-network code— Quantum Lego and more general tensor-network code optimization for biased noise can be done using reinforcement learning [8,9].
- Perfect-tensor code— Artificial intelligence can be used to find AME states [10].
- Hypergraph product (HGP) code— Hypergraph product codes have been optimized against the erasure channel using reinforcement learning [11].
Member of code lists
Primary Hierarchy
Parents
Neural network quantum code
References
- [1]
- T. Fösel, P. Tighineanu, T. Weiss, and F. Marquardt, “Reinforcement Learning with Neural Networks for Quantum Feedback”, Physical Review X 8, (2018) arXiv:1802.05267 DOI
- [2]
- J. Bausch and F. Leditzky, “Quantum codes from neural networks”, New Journal of Physics 22, 023005 (2020) arXiv:1806.08781 DOI
- [3]
- H. P. Nautrup, N. Delfosse, V. Dunjko, H. J. Briegel, and N. Friis, “Optimizing Quantum Error Correction Codes with Reinforcement Learning”, Quantum 3, 215 (2019) arXiv:1812.08451 DOI
- [4]
- J. Olle, R. Zen, M. Puviani, and F. Marquardt, “Simultaneous Discovery of Quantum Error Correction Codes and Encoders with a Noise-Aware Reinforcement Learning Agent”, (2024) arXiv:2311.04750
- [5]
- Z. Wang and H. Tang, “Artificial Intelligence for Quantum Error Correction: A Comprehensive Review”, (2024) arXiv:2412.20380
- [6]
- Z. Wang, T. Rajabzadeh, N. Lee, and A. H. Safavi-Naeini, “Automated discovery of autonomous quantum error correction schemes”, (2021) arXiv:2108.02766
- [7]
- Y. Zeng, Z.-Y. Zhou, E. Rinaldi, C. Gneiting, and F. Nori, “Approximate Autonomous Quantum Error Correction with Reinforcement Learning”, Physical Review Letters 131, (2023) arXiv:2212.11651 DOI
- [8]
- V. P. Su, C. Cao, H.-Y. Hu, Y. Yanay, C. Tahan, and B. Swingle, “Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning”, (2023) arXiv:2305.06378
- [9]
- C. Mauron, T. Farrelly, and T. M. Stace, “Optimization of Tensor Network Codes with Reinforcement Learning”, (2023) arXiv:2305.11470
- [10]
- C. Ruiz-Gonzalez, S. Arlt, J. Petermann, S. Sayyad, T. Jaouni, E. Karimi, N. Tischler, X. Gu, and M. Krenn, “Digital Discovery of 100 diverse Quantum Experiments with PyTheus”, Quantum 7, 1204 (2023) arXiv:2210.09980 DOI
- [11]
- B. C. A. Freire, N. Delfosse, and A. Leverrier, “Optimizing hypergraph product codes with random walks, simulated annealing and reinforcement learning”, (2025) arXiv:2501.09622
Page edit log
- Victor V. Albert (2024-01-08) — most recent
Cite as:
“Neural network quantum code”, The Error Correction Zoo (V. V. Albert & P. Faist, eds.), 2024. https://errorcorrectionzoo.org/c/reinforcement_learning