A Neural Decoder for Topological Codes
Quantum Physics
2017-07-20 v1 Disordered Systems and Neural Networks
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.
Cite
@article{arxiv.1610.04238,
title = {A Neural Decoder for Topological Codes},
author = {Giacomo Torlai and Roger G. Melko},
journal= {arXiv preprint arXiv:1610.04238},
year = {2017}
}