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Quantum Error Correction Fault Tolerance
Quantum Machine Learning
A Neural Decoder for Topological Codes
arXiv
Authors: Giacomo Torlai, Roger G. Melko
Year
2016
Paper ID
42961
Status
Preprint
Abstract Read
~2 min
Abstract Words
82
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2016 reference point for readers tracking recent quantum research.
- We present an algorithm for error correction in topological codes that exploits modern machine learning techniques.
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