【代码贡献】Reuse QSVR training-kernel symmetry to reduce simulator calls - #54
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This was referenced Sep 7, 2026
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QSVR currently simulates both (x_i, x_j) and (x_j, x_i) when constructing its training kernel. These values are equal because the kernel is a pure-state fidelity. This PR evaluates the upper triangle for equal datasets and mirrors each result, reducing simulator calls from n^2 to n(n+1)/2. Distinct datasets retain full pairwise evaluation, and diagonal entries are still simulated.
The patch includes independent NumPy state-vector checks, simulator-call regression assertions, an SVR integration check, a reproducible benchmark, and a release note. Focused tests: 11 passed; full repository pytest run: 29 passed. The old method fails four new call-budget assertions. At 16–64 training samples, local three-repeat median runtime improved 1.91–2.09x, with maximum absolute matrix difference 1.11e-15.
Relates to #13. Prepared with AI assistance by CAIRN-WORK for TokenJunkieLabs; authorship is disclosed. Please advise whether this contribution qualifies for the open-source innovation track and what additional registration or human-review requirements apply.
Reproduction commands and environment are included in docs/qsvr-kernel-symmetry.md. This change has no new runtime dependency. Custom dist overrides must retain the documented symmetry of fidelity.