Study the dimension–distance tradeoff obtained by selecting stabilizer subgroups and changing sparse presentations.
A rank22-per-sector hyperplane subgroup of the 80/34/5 CSS image, followed by the specified ordered physical CNOT sequence. Parent coefficient functional269572 in each sector.
The displayed presentation has maximum check weight 13, maximum qubit degree 8, 44 checks and total incidence 486.
The JSON contains weight-5 logical witnesses. Distance confidence: X: upper_bound, Z: upper_bound.
Recorded workflow configuration: GPT-6 Astra + GPT-6.1 Sol. Official tooling: verify/qldpc_verify.py.
Read codes/80-36-5.json. Allocate zero binary matrices H_X and H_Z with 80 columns. For each row list in checks.X or checks.Z, set the listed zero-based columns to 1, preserving row order. This reconstructs the displayed presentation exactly.
Ordered CNOT pairs (control,target), zero-based: [[9,41],[19,60],[23,50],[28,34],[35,2],[37,20],[40,30],[51,61],[59,8],[63,1],[66,29],[67,14],[69,52],[71,11],[73,26],[74,22],[79,18],[54,39],[59,49],[0,72],[29,66],[40,26],[60,2],[67,77],[74,67]].
Parent-basis parity functionals: {"X":269572,"Z":269572}.
Public source: 80-24-8, contributed by @FarLab.
Local validation: the official verify/validate_candidate.py passed, including independent-seed distance refutation.