Neural network code[13] 

Description

An approximate 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].

Parents

Cousin

  • 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].

References

[1]
T. Fösel et al., “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 et al., “Optimizing Quantum Error Correction Codes with Reinforcement Learning”, Quantum 3, 215 (2019) arXiv:1812.08451 DOI
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Zoo Code ID: reinforcement_learning

Cite as:
“Neural network code”, The Error Correction Zoo (V. V. Albert & P. Faist, eds.), 2024. https://errorcorrectionzoo.org/c/reinforcement_learning
BibTeX:
@incollection{eczoo_reinforcement_learning, title={Neural network code}, booktitle={The Error Correction Zoo}, year={2024}, editor={Albert, Victor V. and Faist, Philippe}, url={https://errorcorrectionzoo.org/c/reinforcement_learning} }
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Permanent link:
https://errorcorrectionzoo.org/c/reinforcement_learning

Cite as:

“Neural network code”, The Error Correction Zoo (V. V. Albert & P. Faist, eds.), 2024. https://errorcorrectionzoo.org/c/reinforcement_learning

Github: https://github.com/errorcorrectionzoo/eczoo_data/edit/main/codes/quantum/qubits/reinforcement_learning.yml.