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Krastanov S, Jiang L. Deep Neural Network Probabilistic Decoder for Stabilizer Codes. Sci Rep. 2017;7(1):11003doi: 10.1038/s41598-017-11266-1.
Krastanov, S., & Jiang, L. (2017). Deep Neural Network Probabilistic Decoder for Stabilizer Codes. Scientific reports, 7(1), 11003. https://doi.org/10.1038/s41598-017-11266-1
Krastanov, Stefan, and Jiang, Liang. "Deep Neural Network Probabilistic Decoder for Stabilizer Codes." Scientific reports vol. 7,1 (2017): 11003. doi: https://doi.org/10.1038/s41598-017-11266-1
Krastanov S, Jiang L. Deep Neural Network Probabilistic Decoder for Stabilizer Codes. Sci Rep. 2017 Sep 08;7(1):11003. doi: 10.1038/s41598-017-11266-1. PMID: 28887480; PMCID: PMC5591216.
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Sci Rep. 2017 Sep 08;7(1):11003. doi: 10.1038/s41598-017-11266-1.

Deep Neural Network Probabilistic Decoder for Stabilizer Codes.

Scientific reports

Stefan Krastanov, Liang Jiang

Affiliations

  1. Departments of Physics and Applied Physics, Yale University, New Haven, 06520, Connecticut, USA.
  2. Yale Quantum Institute, Yale University, New Haven, 06520, Connecticut, USA.
  3. Departments of Physics and Applied Physics, Yale University, New Haven, 06520, Connecticut, USA. [email protected].
  4. Yale Quantum Institute, Yale University, New Haven, 06520, Connecticut, USA. [email protected].

PMID: 28887480 PMCID: PMC5591216 DOI: 10.1038/s41598-017-11266-1

Abstract

Neural networks can efficiently encode the probability distribution of errors in an error correcting code. Moreover, these distributions can be conditioned on the syndromes of the corresponding errors. This paves a path forward for a decoder that employs a neural network to calculate the conditional distribution, then sample from the distribution - the sample will be the predicted error for the given syndrome. We present an implementation of such an algorithm that can be applied to any stabilizer code. Testing it on the toric code, it has higher threshold than a number of known decoders thanks to naturally finding the most probable error and accounting for correlations between errors.

References

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  2. Science. 2017 Feb 10;355(6325):602-606 - PubMed
  3. Phys Rev Lett. 2017 Jul 21;119(3):030501 - PubMed
  4. Phys Rev A. 1996 Aug;54(2):1098-1105 - PubMed

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