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• W3123065342 abstract "With the advent of noisy intermediate-scale quantum (NISQ) devices, practical quantum computing has seemingly come into reach. However, to go beyond proof-of-principle calculations, the current processing architectures will need to scale up to larger quantum circuits which in turn will require fast and scalable algorithms for quantum error correction. Here we present a neural network based decoder that, for a family of stabilizer codes subject to depolarizing noise, is scalable to tens of thousands of qubits (in contrast to other recent machine learning inspired decoders) and exhibits faster decoding times than the state-of-the-art union find decoder for a wide range of error rates (down to 1%). The key innovation is to autodecode error syndromes on small scales by shifting a preprocessing window over the underlying code, akin to a convolutional neural network in pattern recognition approaches. We show that such a preprocessing step allows to effectively reduce the error rate by up to two orders of magnitude in practical applications and, by detecting correlation effects, shifts the actual error threshold to $p_{rm threshold} = 0.167(0)$, some ten percent higher than the threshold of conventional error correction algorithms such as union find or minimum weight perfect matching. An in-situ implementation of such machine learning-assisted quantum error correction will be a decisive step to push the entanglement frontier beyond the NISQ horizon." @default.
• W3123065342 created "2021-02-01" @default.
• W3123065342 creator A5029938494 @default.
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• W3123065342 date "2021-01-18" @default.
• W3123065342 modified "2023-09-27" @default.
• W3123065342 title "Scalable Neural Decoder for Topological Surface Codes" @default.
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• W3123065342 hasPublicationYear "2021" @default.
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