Summary

• qqmul (Hamilton product) and qvmul (vector rotation) both used np.dot/np.cross on 3-vectors, whose generic dispatch overhead - built for arbitrary shapes/broadcasting - dominates cost at this size. Same pattern as isR/trnorm/tr2adjoint in perf(base): speed up isR/ishom/ishom2 orthogonality checks #213/perf(base): speed up trnorm and tr2adjoint #214.
• qqmul: replaced with explicit scalar Hamilton-product arithmetic. Bit-identical to the prior result (verified to ~1 ULP over 2000 random trials).
• qvmul: replaced the q * pure(v) * conj(q) sandwich (two full Hamilton products through qqmul/qpure/qconj, each with its own getvector re-validation, and each Hamilton product wasting work on a zero scalar part) with the closed-form rotation identity v' = v + 2s(w x v) + 2 w x (w x v) for q = (s, w). This is a genuinely different (though mathematically equivalent) formula, not just a re-expression of the same one - so it is not bit-identical, verified instead to ~1e-14 absolute over 2000 random trials (with v scaled to magnitude ~10), well within floating-point noise.
• Net effect: qqmul ~10x faster (15.0us -> 1.4us isolated), qvmul ~24x faster (33.4us -> 1.4us isolated). End to end: Q1 * v drops from ~33us to ~4us (~9x). Q1 * Q2 drops from ~19us to ~10us (~2x) - qqmul itself is no longer the bottleneck there; the remainder is UnitQuaternion construction overhead (in particular qunit()'s np.linalg.norm/np.r_ calls), a separate, un-addressed candidate for a possible future fix in the same spirit, not attempted here.

Test plan

• pytest tests/ - 343 passed
• qqmul verified against the prior implementation over 2000 random quaternion pairs (max diff ~1 ULP, i.e. bit-identical modulo floating-point operation ordering)
• qvmul verified against the prior implementation over 2000 random (quaternion, vector) pairs, vector magnitude ~10 (max abs diff ~1.4e-14)

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