Machine-learning-assisted correction of correlated qubit errors in a topological code

Paul Baireuther, Thomas E. O’Brien, Brian Tarasinski, C. W. J. Beenakker

Quantum 2018 article cited by 141

Source Open-access PDF More in Physics & Astronomy

importance 7/10 ai assisted Quantum Computing Algorithms and Architecture quantum computing stage: research

Problem

Fault-tolerant quantum computation requires efficient detection and correction of errors in encoded quantum information, and standard decoders miss correlations between bit-flip and phase-flip errors.

Why it matters

Error correction is a prerequisite for any practical, fault-tolerant quantum computer.

What the AI did

A recurrent neural network with long short-term memory layers was trained on only experimentally accessible data to decode the surface code, without needing a noise model.

Results

Performance above the established minimum-weight perfect matching (blossom) decoder, sustained over a large number of error correction cycles.

Impact

Proposed as a practical decoder for forthcoming experimental realizations of the surface code per the abstract.

Record

Venue	Quantum
Published	2018
Work type	article
Authors	Paul Baireuther, Thomas E. O’Brien, Brian Tarasinski, C. W. J. Beenakker
Citations	141 (OpenAlex, at import time)
References	54
Model or method	recurrent neural network with LSTM layers
Organization	not stated in source
Primary source	https://doi.org/10.22331/q-2018-01-29-48
Open access	gold
Keywords	quantum error correction, surface code, recurrent neural network, LSTM, decoder

Importance rating

7 / 10

significant — thousands to millions affected

Error correction is a prerequisite for fault-tolerant quantum computing, a foundational scientific field; abstract gives no scale figures.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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