Neural Decoder for Topological Codes

Giacomo Torlai and Roger G. Melko
Phys. Rev. Lett. 119, 030501 – Published 18 July 2017
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Abstract

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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  • Received 20 October 2016

DOI:https://doi.org/10.1103/PhysRevLett.119.030501

© 2017 American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & TechnologyStatistical Physics & Thermodynamics

Authors & Affiliations

Giacomo Torlai and Roger G. Melko

  • Department of Physics and Astronomy, University of Waterloo, Ontario N2L 3G1, Canada and Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada

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Issue

Vol. 119, Iss. 3 — 21 July 2017

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