Study the dimension–distance tradeoff obtained by selecting stabilizer subgroups and changing sparse presentations.
Take the explicitly recorded coefficient hyperplane(s) in the 80/44/5 parent, then apply the recorded ordered physical CNOT sequence.
The displayed presentation has maximum check weight 20, maximum qubit degree 12, 35 checks and total incidence 610.
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/80-45-5.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: [[23,64],[35,65],[37,44],[48,33],[49,26],[61,77],[72,13],[22,1],[22,78],[52,53],[15,67],[22,11]].
Parent-basis parity functionals: {"X":20279,"Z":null}.
Source sector rows as integer support masks (bit q is qubit q): {"X":["12102506812355261760010","83831805625108203323394","765028058516213328057344","10081145710431662342168","45051705255894123088008","47242833274631977961857","37939201463722137151760","377789544379141347889356","114572728578233200551956","161870274983536432840768","162481335810094050324514","180206396550827542119944","323934049330023165331042","113670002626300085936197","457248672104818386434179","761060959228182877745223","1095688499556747046843650"],"Z":["81461686520672921092352","26600206292566624174179","95037823661497022189572","297503859532191981636","1317481304082220320","11144247490658739945493","89946396163219126782468","359273981413080821792768","4796276182265075870744","38079845360929375556004","96822641324253252682368","646983017455779909871617","935083905251118701875200","56809054306440013218440","41995169816876970672144","151150640702114590998536","316445754609636399392050","605422226643688827652163"]}. Apply the CNOTs above in order: X[target] ^= X[control], Z[control] ^= Z[target], then choose the displayed basis within the resulting row spaces.
Public source: 80-24-8, contributed by @FarLab.
Local validation: the official verify/validate_candidate.py passed, including independent-seed distance refutation.