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GBSKernels v0.1.0

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@chaofanzhougit chaofanzhougit released this 06 Jul 13:09
· 23 commits to main since this release
v0.1.0
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GBSKernels v0.1.0 — initial public release

A GPU-native, batched library of the four #P-hard matrix functions behind photonic quantum sampling — the permanent, hafnian, loop hafnian, and torontonian — with an explicit floating-point accuracy model ranging from native double precision to rigorous a-posteriori error bounds.

Highlights

  • All four functions, batched, on CPU and CUDA, each validated against independent combinatorial ground truth.
  • Explicit precision model: fp64 (measured accuracy boundary), dd (double-double), ref (arbitrary precision), auto (heuristic cancellation guard), and certified — a rigorous per-evaluation error bound whose enclosure of the true value is a hard test invariant.
  • Structure-aware kernels: a repeated-row finite-difference sieve for the loop hafnian and a recursive prefix-Cholesky torontonian (with a single-large mode to 32 modes).
  • A conditional GBS sampler validated distributionally against The Walrus.
  • A five-layer verification suite (independent ground truth, differential oracle, property-based invariants, end-to-end physics, numerical-accuracy characterization).

Install

pip install gbskernels      # once on PyPI — CPU library, numpy + mpmath only

The CUDA extension is a separate, optional build (bindings/).

Complements The Walrus; Apache-2.0 licensed.

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