Krylov polynomials and quantum query complexity

We show that the query complexity of preparing f ( H )| ψ 0 ⟩ is controlled by the optimal L 2 ( μ ) polynomial approximation degree of f , where μ is the spectral measure induced by ( H , | ψ 0 ⟩). Equivalently, the minimal number of queries is bounded below by the smallest Krylov–Favard truncation achieving error ε and bounded above, up to QSVT normalization overhead, by a linear function of that degree. This state-aware formulation sharpens worst-case bounds, unifies orthogonal-polynomial Krylov methods with quantum query complexity, and highlights how input-dependent spectral structure can yield substantial savings over uniform approximation schemes.

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Publication Details

Journal
Physics Letters A
Published
2026-03-21
DOI
https://doi.org/10.1016/j.physleta.2026.131601
Primary Topic
Advanced Combinatorial Mathematics
Type
article
Field-Weighted Citation Impact
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article

Krylov polynomials and quantum query complexity

Kiran Adhikari
Physics Letters A
Advanced Combinatorial Mathematics
article

Krylov polynomials and quantum query complexity

Kiran Adhikari
article en

Abstract

We show that the query complexity of preparing f ( H )| ψ 0 ⟩ is controlled by the optimal L 2 ( μ ) polynomial approximation degree of f , where μ is the spectral measure induced by ( H , | ψ 0 ⟩). Equivalently, the minimal number of queries is bounded below by the smallest Krylov–Favard truncation achieving error ε and bounded above, up to QSVT normalization overhead, by a linear function of that degree. This state-aware formulation sharpens worst-case bounds, unifies orthogonal-polynomial Krylov methods with quantum query complexity, and highlights how input-dependent spectral structure can yield substantial savings over uniform approximation schemes.

Physics Letters AVol. 584
Center for Theoretical Physics (PL)
Openalex Percentile: Top 12%
Advanced Combinatorial Mathematics
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