Entry facet
Decoders
Algorithms, noise models, applicability, and performance notes.
Minimum-weight perfect matching
mwpmstandardRepresents detection events as vertices of a weighted graph and pairs them by minimum total weight; the pairing determines a likely correction chain.
Type: matching · Noise setting: periodic honeycomb detector graphs with repeated parity-measurement faults
For this code: Plaquette changes populate two interleaved spacetime matching graphs. Correlation-aware matching uses the named parity-measurement circuit and its edge probabilities; the reported threshold range therefore belongs to that circuit/noise/decoder triple, not to the abstract schedule.
References: higgott2022pymatching, gidney2021fault
Minimum-weight perfect matching
mwpmresearch-gradeRepresents detection events as vertices of a weighted graph and pairs them by minimum total weight; the pairing determines a likely correction chain.
Type: matching · Noise setting: planar SD6, SI1000, EM3, and Majorana-native detector models
For this code: Boundaries, hook propagation, correlated parity faults, and measurement normalization change the detector graph. Planar and Majorana benchmarks must be compared within their stated circuit models rather than transferred to the torus snapshot.
References: higgott2022pymatching, gidney2022planar, paetznick2023majorana