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LemmaStatement: Literature-sourcedProof: AI-adaptedPipeline-generated
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Rational encoding preserves a promised local-Hamiltonian gap

Statement

Fix k and let H=∑j=1mHj be an encoded k-local Hamiltonian with m=poly⁡(n), local dimension at most D=2k, and algebraic entries supplied by polynomial-time Cauchy procedures. For every rational τ>0, a deterministic polynomial-time algorithm outputs rational Hermitian local matrices H~j with ∑j∥Hj−H~j∥≤τ. Consequently ∣λmin⁡(H)−λmin⁡(H~)∣≤τ. If the original promise thresholds are a<b with gap Δ=b−a, choose τ≤Δ/8 and output thresholds a+τ,b−τ; the mapped YES and NO instances remain on their respective promise sides with gap at least 3Δ/4. A concrete polynomial running-time conclusion requires the stated polynomial-time Cauchy bounds for each algebraic entry encoding.

Facts & Assumptions

Given: The finite local matrices and encodings, fixed k, polynomial m, and rational τ>0.

[F1]

Effective finite matrix encodings for local Hamiltonians: each entry can be approximated by rational real and imaginary parts in polynomial bit time at requested precision.

[F2]

Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely: Hermitian matrices have finite orthonormal eigenbases and their least eigenvalue is the minimum unit Rayleigh quotient.

[F3]

The operator norm is 0 on the zero domain and otherwise equals the largest singular value, attained at a right-singular vector: the operator norm is the largest singular value and is bounded by the Frobenius norm.

[F4]

Encoded k-local Hamiltonian promise problem: the explicit instance has finitely many fixed-arity Hermitian terms whose sum defines the promised Hamiltonian.

Proof

1.1F1F3F4givenconstruct

Request each of the at most D2 entries of each Hj to absolute complex error at most τ/(2mD). Replace each entry by its rational approximation and symmetrize the matrix by (A+A∗)/2; this preserves rationality and Hermiticity, and does not increase the Frobenius error beyond the entrywise bound. The Frobenius norm of a D×D error matrix is at most D times its largest entry error, so [F3] gives ∥Hj−H~j∥≤τ/(2m). The precision request has O(log⁡(mD/τ)) bits beyond the input description and is computed in polynomial time by [F1]. The term list and common embedding are those of [F4].

2.1F2step 1.1algebra∎

Identity embedding preserves operator norm, so ∥H−H~∥≤∑j∥Hj−H~j∥≤τ/2≤τ. For each unit v, ∣⟨Hv,v⟩−⟨H~v,v⟩∣≤τ; taking minima over the same unit sphere in both directions and using [F2] yields ∣λmin⁡(H)−λmin⁡(H~)∣≤τ. If λmin⁡(H)≤a, then λmin⁡(H~)≤a+τ; if λmin⁡(H)≥b, then λmin⁡(H~)≥b−τ. Their new gap is Δ−2τ≥3Δ/4.

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