♫ Note

SIMD ADC fast-scan & score-aware quantization (FAISS PQ4, ScaNN)

FAISS (Meta): github.com/facebookresearch/faiss. The quantization reference — PQ/OPQ/IVFADC, IndexBinaryFlat, and crucially the PQ4 "fast scan": 4-bit PQ codes + an in-register lookup table via SIMD shuffle (pshufb/vpshufb), 16 table lookups per instruction. This is the fast ADC we never tested (our 054 used scalar ADC, which lost to popcount).
ScaNN (Google): github.com/google-research/google-research/tree/master/scann. Paper "Accelerating Large-Scale Inference with Anisotropic Vector Quantization" (ICML 2020). Two ideas: score-aware / anisotropic quantization (codebook loss weighted to preserve the inner-product ranking, not reconstruction) + the same SIMD ADC. Their AVQ beats plain PQ on recall-per-bit.

Why tracked (relevance to us)

These are the two things that could change our headline verdict:

SIMD ADC could beat popcount on QPS. We concluded scalar PQ loses to VPOPCNTDQ (054, 011) and parked SIMD (050). PQ4/ScaNN's pshufb LUT is the proven way to make gather-based ADC fast. The safe-Rust route is core::simd::Simd::swizzle_dyn, which lowers to pshufb/tbl (nightly, no unsafe). The decisive untested experiment.
Score-aware quantization is a better-codes objective than our rotation+residual (which minimize reconstruction, not ranking) — a recall-per-bit frontier we haven't tried.

Our scalar PQ/OPQ results: [[history 054]], [[history 056]]. SIMD ceiling: [[history 050]].
RaBitQ ([[002-rabitq]]) — the binary-side equivalent of "better codes".