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Paper 1
A Neural Decoder for Topological Codes
Giacomo Torlai, Roger G. Melko
- Year
- 2016
- Journal
- arXiv preprint
- DOI
- arXiv:1610.04238
- arXiv
- 1610.04238
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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Proceedings 9th Workshop on Quantum Physics and Logic
Ross Duncan, Prakash Panangaden
- Year
- 2014
- Journal
- arXiv preprint
- DOI
- arXiv:1407.8427
- arXiv
- 1407.8427
This volume contains the proceedings of the ninth workshop on Quantum Physics and Logic (QPL2012) which took place in Brussels from the 10th to the 12th of October 2012. QPL2012 brought together researchers working on mathematical foundations of quantum physics, quantum computing, and spatio-temporal causal structures. The particular focus was on the use of logical tools, ordered algebraic and category-theoretic structures, formal languages, semantical techniques, and other computer science methods for the study of physical behaviour in general.
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