Scalable Neural Decoder for Topological Surface Codes - Kölner UniversitätsPublikationsServer

Meinerz, Kai, Park, Chae-Yeun and Trebst, Simon ORCID: 0000-0002-1479-9736 (2022). Scalable Neural Decoder for Topological Surface Codes. Phys. Rev. Lett., 128 (8). COLLEGE PK: AMER PHYSICAL SOC. ISSN 1079-7114

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DOI:10.1103/PhysRevLett.128.080505

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 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 and syndrome measurement errors, 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 2 orders of magnitude in practical applications and, by detecting correlation effects, shifts the actual error threshold up to fifteen percent higher than the threshold of conventional error correction algorithms such as union find or minimum weight perfect matching, even in the presence of measurement errors. An in situ implementation of such a machine learning-assisted quantum error correction will be a decisive step to push the entanglement frontier beyond the NISQ horizon.

Item Type:

Journal Article

Creators:

Creators	Email	ORCID	ORCID Put Code
Meinerz, Kai	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Park, Chae-Yeun	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Trebst, Simon	UNSPECIFIED	orcid.org/0000-0002-1479-9736	UNSPECIFIED