Pith. sign in

REVIEW 1 cited by

Fault-tolerant thresholds for the surface code in excess of 5% under biased noise

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1907.02554 v2 pith:MGPU6VTJ submitted 2019-07-04 quant-ph cond-mat.str-el

classification quant-phcond-mat.str-el
keywords noisebiaseddecoderdephasingfault-tolerantcodeerrorsexcess
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

Noise in quantum computing is countered with quantum error correction. Achieving optimal performance will require tailoring codes and decoding algorithms to account for features of realistic noise, such as the common situation where the noise is biased towards dephasing. Here we introduce an efficient high-threshold decoder for a noise-tailored surface code based on minimum-weight perfect matching. The decoder exploits the symmetries of its syndrome under the action of biased noise and generalises to the fault-tolerant regime where measurements are unreliable. Using this decoder, we obtain fault-tolerant thresholds in excess of $6\%$ for a phenomenological noise model in the limit where dephasing dominates. These gains persist even for modest noise biases: we find a threshold of $\sim 5\%$ in an experimentally relevant regime where dephasing errors occur at a rate one hundred times greater than bit-flip errors.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploiting biased noise in variational quantum models

    quant-ph 2025-10 conditional novelty 6.0 of 10

    Twirling amplitude-damping noise into uniform Pauli/depolarising channels reduces expressivity and gradient magnitudes, while preserving the noise bias yields better VQA optimisation in the studied models.

Pith tools