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Guides exact-MWPM graphlike decoding with fusion-blossom, including erasure-aware matching updates, while keeping clear boundaries with PyMatching and non-graphlike decoder lanes.
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Based on SOC occupation classification
Provides guidance for choosing among QEC decoder backends such as ldpc, PyMatching, fusion-blossom, Chromobius, MWPF, tesseract-decoder, and MQT QECC based on artifact shape, code family, noise model, and validation goals.
Provides guidance for decoding graphlike Stim detector models and sampled trajectories with PyMatching, using the repo's registry-backed decoder service and explicit graphlike-DEM constraints.
Guides search-based decoder planning with tesseract-decoder for small or medium QEC studies, keeping its baseline value separate from scalable matching or hypergraph lanes.
Guides use of MQT QECC for toolkit-native code, decoder, color-code, and state-preparation workflows while keeping clear boundaries with Stim-native and qLDPC-native lanes.
Guides reproduction of folded-H surface-code cultivation workflows from the MSC_foldedH repo, including sample-circuit inspection, handoff runs, and boundaries around the currently supported stabilizer-based escape path.
Guides staged reproduction of magic-state-cultivation workflows from Strilanc's reference repo, including circuit generation, long-run statistics collection, and plot regeneration without collapsing those stages together.
| name | decoding-with-fusion-blossom |
| description | Guides exact-MWPM graphlike decoding with fusion-blossom, including erasure-aware matching updates, while keeping clear boundaries with PyMatching and non-graphlike decoder lanes. |
| version | 0.1.0 |
| author | QEC Research Skills |
| license | MIT |
| tags | ["QEC","fusion-blossom","MWPM","Decoders"] |
| dependencies | ["qec-research","building-stim-circuits","choosing-qec-decoders"] |
Use this skill when the task is still a matching-graph problem but you want the
fusion-blossom lane specifically:
PyMatching,Stim workflows honest about when erasure information can be
represented as matching-weight updates,fusion-blossom as "just another matching decoder",Use this skill when the workload is:
Prefer decoding-with-pymatching when
the goal is the repo's current integrated matching lane.
Prefer choosing-qec-decoders when the
main question is whether the problem should stay in the matching lane at all.
Do not use this skill to flatten hypergraph, color-code-specialized, or
full-circuit heralded-erasure workloads that belong to mwpf, chromobius, or
other richer lanes.
This repo does not yet ship a fusion-blossom adapter or smoke test.
The correct current use is:
fusion-blossom workflow precisely,Expected future repo inputs:
DetectorModelBundle,TrajectoryBundle,Assumptions to make explicit:
research-state.yaml for the current
decoder-roadmap assumptions.servers/resources/decoder_capability_matrix.yaml
and servers/resources/decoder_workload_templates.yaml.PyMatching is insufficient for the task.Before closing work:
Problem: fusion-blossom is proposed for a workload whose main difficulty is
hypergraph structure or full-circuit heralding, not exact matching.
Fix: route back through
choosing-qec-decoders and check whether
the problem should leave the matching lane entirely.
Problem: a heralded-erasure workflow is described as matching-compatible without checking whether the side information survives as edge-weight updates.
Fix: say explicitly whether the erasure model is matching-representable. If it
is not, move toward mwpf or a protocol-specific erasure lane.
Problem: a user-facing answer sounds like the repo can already run
fusion-blossom.
Fix: keep the language narrow. This skill distills the upstream lane and how to adopt it; it does not mean the adapter already exists here.