The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Neural Decoder for Topological Codes
Open accessAbstract
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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Multilayer feedforward networks are universal approximators
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Training Products of Experts by Minimizing Contrastive Divergence
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Surface codes: Towards practical large-scale quantum computation
Fowler, A. G., Mariantoni, M., Martinis, J. M., & Cleland, A. N. (2012) · Physical Review A · doi:10.1103/physreva.86.032324 · Cited by 3,059
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Solving the quantum many-body problem with artificial neural networks
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Neural Networks: Tricks of the Trade: Second Edition
(2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8 · Cited by 2,116
Machine learning phases of matter
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Self-citation
Cited by
Almost-linear time decoding algorithm for topological codes
Delfosse, N., & Nickerson, N. H. (2021) · Quantum · doi:10.22331/q-2021-12-02-595 · Cited by 242
Ultrahigh Error Threshold for Surface Codes with Biased Noise
Tuckett, D. K., Bartlett, S. D., & Flammia, S. T. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.050505 · Cited by 216
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Higgott, O., & Gidney, C. (2025) · Quantum · doi:10.22331/q-2025-01-20-1600 · Cited by 180
Artificial intelligence and machine learning for quantum technologies
Krenn, M., Landgraf, J., Foesel, T., & Marquardt, F. (2023) · Physical Review A · doi:10.1103/physreva.107.010101 · Cited by 162
Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines
Cheng, S., Chen, J., & Wang, L. (2018) · Entropy · doi:10.3390/e20080583 · Cited by 104
Metadata
- Authors
- Torlai, G., & Melko, R. G.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2017-07-18
- Volume
- 119
- Issue
- 3
- Pages
- 030501-030501
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2533940598
- Cited by
- 194
- References
- 9
- FWCI
- 14.27
- Type
- journal article
- Language
- en
- Open access
- green
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