Study the dimension–distance tradeoff obtained by selecting stabilizer subgroups and changing sparse presentations.
Rank-one-per-sector stabilizer subgroup of the public @FarLab 2BGA code [[80,24,8]] on ab(5,2,2,2). Parent exact Git blob43dae9d893a9f2b5c37480d6df4dbc2e10ac423f, JSON SHA256a35525e02f719c21c60a548078777d280286010cd0ef5f5eb5fae93db27653a6, at https://github.com/unitaryfoundation/qldpc-challenge/blob/15e58eea2bba862eb49a8a162fa801f81d027f5a/codes/80-24-8.json. Select these zero-indexed rows directly from its literal checks: X=[24, 32, 5, 16, 25, 4, 9, 8, 27, 35, 36, 28, 21, 15, 0, 20, 29, 3, 38, 13, 30, 23, 2, 39, 12, 22, 31];Z=[22, 32, 7, 27, 23, 6, 33, 9, 24, 10, 34, 4, 35, 11, 21, 3, 30, 19, 2, 13, 31, 14, 1, 28, 0, 39, 17]. The resulting ranks are27+27 and actual(n,k,d,w,D,M,I)=[80, 26, 7, 10, 8, 54, 540].
The displayed presentation has maximum check weight 10, maximum qubit degree 8, 54 checks and total incidence 540.
The JSON contains weight-7 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/80-26-7.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.
Public source: 80-24-8, contributed by @FarLab.
Local validation: the official verify/validate_candidate.py passed, including independent-seed distance refutation.