REVIEW 4 cited by
Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network
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
read the original abstract
Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google's Sycamore quantum processor for distance 3 and 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk, leakage, and analog readout signals, and sustains its accuracy far beyond the 25 cycles it was trained on. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers.
Forward citations
Cited by 4 Pith papers
-
Physics-Informed Graph-Neural Decoding of the Surface Code: the Logical Signal as an Exact Topological Pairing
The logical-error signal in a surface-code decoder is an exact relative-cohomology pairing of the syndrome with a boundary-fixed harmonic coordinate, evaluated as the current difference between two boundary sinks.
-
Improved belief propagation is sufficient for real-time decoding of quantum memory
Relay-BP, a message-passing decoder using disordered memory strengths and relay ensembling, matches or beats benchmark decoders for bivariate-bicycle and surface codes within a real-time iteration budget.
-
Learning Neural Decoding with Parallelism and Self-Coordination for Quantum Error Correction
A transformer-based decoder trained on local window labels learns to output per-window logical corrections that can be XORed across sliding windows, enabling parallel decoding with accuracy slightly above belief match...
-
Synchronization for Fault-Tolerant Quantum Computers
Active and Hybrid synchronization policies cut logical error rates by up to 2.4x and 3.4x compared to passive waiting, by distributing idle time across syndrome generation rounds.
Discussion (0). Continue with ORCID to comment.