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Krastanov
marked this pull request as ready for review
September 25, 2026 05:57
Krastanov
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September 25, 2026 06:00
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This was a quick and dirty benchmark I wanted to run to look at various bitpacking strategies. I have not actually validated myself that any of this is done correctly, please disregard for the moment.
AI slop below:
PR 204 already pads each tableau major to 256 bits. This adds a reproducible comparison of its storage and public API with QuantumClifford.jl and a Rust prototype using 8/16/32/64/128-bit words and both packing axes. Production storage is unchanged.
The harness checks common deterministic inputs against an independent Pauli reference, rejects missing configurations, records unsupported cases, and runs workers sequentially across 16 sizes and three process launches. The report includes 32,400 timing samples, 1,170 additional arbitrary-input validation cases, raw results, plots, source/executable hashes and a replayable Julia dependency lock.
On the measured Apple M5 Pro at 1,024 qubits:
The results support keeping generator-packed columns, amortizing row conversion, and evaluating QuantumClifford's phase kernel independently. Compact padding helps the experimental small-tableau cases; larger-tableau gains are modest and mixed. QC
fastcolumntransposes words, while ppvm columns pack generator bits; the report explains this distinction and the comparison limits.Full report · Run instructions
Validation: full Rust workspace tests (1,938 passed, three ignored), focused tableau and candidate tests, workspace Clippy/check, Rust formatting, Python lint/format, all benchmark checks, and exact Julia dependency replay. Local Rust is 1.96.0. The first upstream CI run uses newer Clippy and fails on
chunks_exact_to_as_chunksin unchangedcrates/stim-parser/src/pipeline/lower.rs:440; that unrelated parser code is outside this study.Stacked on #204, pinned at
3befaf1b597033138d6c1bab394b967b09cd57fb. QuantumClifford is pinned at1e553b25fdf12c67b55ccedfbc2f48f4dea78fcc.