Scalable Neural Decoder for Topological Surface Codes

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 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 two 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 machine learning-assisted quantum error correction will be a decisive step to push the entanglement frontier beyond the NISQ horizon.

10 pages, 10 figures, 5 tables

References in corpus (8)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Charge insensitive qubit design derived from the Cooper pair box
• Surface codes: Towards practical large-scale quantum computation
• Transmon platform for quantum computing challenged by chaotic fluctuations
• Gate-error analysis in simulations of quantum computers with transmon qubits
• Subsystem codes with high thresholds by gauge fixing and reduced qubit overhead
• General tensor network decoding of 2D Pauli codes
• Hierarchical decoding to reduce hardware requirements for quantum computing

Cited by in corpus (7)

• Artificial Intelligence and Machine Learning for Quantum Technologies
• Improved decoding of circuit noise and fragile boundaries of tailored surface codes
• Parallel window decoding enables scalable fault tolerant quantum computation
• A scalable and fast artificial neural network syndrome decoder for surface codes
• A local pre-decoder to reduce the bandwidth and latency of quantum error correction
• Techniques for combining fast local decoders with global decoders under circuit-level noise
• A NEAT Quantum Error Decoder