Journal
PHYSICAL REVIEW LETTERS
Volume 119, Issue 3, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.119.030501
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Funding
- NSERC
- CRC program
- Ontario Trillium Foundation
- Perimeter Institute for Theoretical Physics
- National Science Foundation [NSF PHY-1125915]
- Industry Canada
- Province of Ontario through the Ministry of Research Innovation
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We present an algorithm for error correction in topological codes that exploits modern machine learning techniques. Our decoder is constructed from a stochastic neural network called a Boltzmann machine, of the type extensively used in deep learning. We provide a general prescription for the training of the network and a decoding strategy that is applicable to a wide variety of stabilizer codes with very little specialization. We demonstrate the neural decoder numerically on the well-known two-dimensional toric code with phase-flip errors.
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