Optimization of tensor network codes with reinforcement learning

Tensor network codes enable structured construction and manipulation of stabilizer codes out of small seed codes. Here, we apply reinforcement learning (RL) to tensor network code geometries and demonstrate how optimal stabilizer codes can be found. Using the projective simulation framework, our RL...

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Bibliographic Details
Published inNew journal of physics Vol. 26; no. 2; pp. 23024 - 23034
Main Authors Mauron, Caroline, Farrelly, Terry, Stace, Thomas M
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.02.2024
Subjects
Codes
Error correction & detection
Mathematical analysis
Optimization
Physics
quantum error correction
reinforcement learning
tensor networks
Tensors
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ISSN1367-2630
1367-2630
DOI10.1088/1367-2630/ad23a6

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Summary:Tensor network codes enable structured construction and manipulation of stabilizer codes out of small seed codes. Here, we apply reinforcement learning (RL) to tensor network code geometries and demonstrate how optimal stabilizer codes can be found. Using the projective simulation framework, our RL agent consistently finds the best possible codes given an environment and set of allowed actions, including for codes with more than one logical qubit. The agent also consistently outperforms a random search, for example finding an optimal code with a 10 % frequency after 1000 trials, vs a theoretical 0.16 % from random search, an improvement by a factor of 65.
Bibliography:NJP-116351.R2
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ISSN:1367-2630
1367-2630
DOI:10.1088/1367-2630/ad23a6