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AlgorithmAlgorithm: Literature-sourcedProof: Not applicablePipeline-generatedjudge pass (gpt-6-luna)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Sparse-oracle finite Gibbs purification procedure

Definition

Input: a nonzero finite-dimensional Hilbert space of dimension d, a Hermitian Hamiltonian matrix K, an inverse-energy parameter β>0 such that H=βK is dimensionless, the ordered reference preparation ∣Φd⟩ from Finite Gibbs density and its canonical purification, an output vector-error tolerance εout>0, and a rational amplification-failure target 0<ρ<1. The input contract must also supply a rational shift-confidence target 0<δsh<1, a finite call cap Qcap, and rational sampler precision 0<ν≤δsh2/(1012d(Qcap+1)2) as required by Coherent spectral minimum and a frozen Gibbs energy shift, as well as every sparse-oracle, block-encoding, controlled-evolution, inverse, norm-bound, energy-shift, coefficient-generation, implementation-error budget, synthesis-precision, reference-preparation, and cost object required by Sparse-oracle Gibbs-state purification. No matrix exponential, trace, exact spectral minimum, or gate is free input.

Procedure: invoke the normalized-output branch of Sparse-oracle Gibbs-state purification on K with coefficient η=β, the stated vector-error and amplification-failure targets, the supplied shift-confidence target δsh, and complete access/cost certificates. Retain its frozen energy shift and all declared finite subroutines. Measure only its specified good/bad flag. On a good branch return the normalized system-reference vector; on a cap failure return the explicit output FAIL. If the certified shift event fails, do not assert the Gibbs approximation guarantee; any overall failure probability includes the supplied δsh shift-confidence budget.

Output: either FAIL, or a normalized vector-register state in the successful branch, with the error and success guarantees supplied by Gibbs purification error and resource bound. An identity energy shift changes neither the normalized Gibbs density nor its canonical purification, since e−(βK+cI)=e−ce−βK and the trace is multiplied by the same scalar. This is a mathematical state-preparation procedure under the stated finite access model; it is not a physical bath or thermalization process.

Depends on

Dependency tree · 2 levels

Sources