Optimal Quantum Eigenvalue Transformation via Linear Combinations of Hermitian Matrices

• URL: http://arxiv.org/abs/2607.25812v1
• Date: Tue, 28 Jul 2026 14:58:02 GMT
• Title: Optimal Quantum Eigenvalue Transformation via Linear Combinations of Hermitian Matrices
• Abstract summary: We discover two linear-combination-of-Hermitian-matrices (LCHM) formulations to achieve a general non-normal matrix eigenvalue transformation $g(A)$.<n>For the Hermitian $X_= cosL+sinH$, the Weyl LCHM formula expresses $g(A)$ via integrating $g(mathrmemathrmi (X_pmmathrmi(I-X_2)1/2)$.<n>For the matrix power $g(
• Score: 4.089626331487613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We discover two complementary linear-combination-of-Hermitian-matrices (LCHM) formulations to achieve a general non-normal matrix eigenvalue transformation $g(A)$. Firstly, for $A=L+\mathrm{i} H$ with Hermitian $L$ and $H$, the vanilla LCHM formula represents $g(A)$ as a kernel integral of $g(\mathrm{i}(H+kL))$, and it contains linear-combination-of-Hamiltonian-simulation (LCHS) [An, Liu, Lin, Phys. Rev. Lett. 2023] as the special case for matrix exponentials. Secondly, for the angular Hermitian $X_θ= \cosθL+\sinθH$, the Weyl LCHM formula expresses $g(A)$ via integrating $g(\mathrm{e}^{\mathrm{i}θ} (X_θ\pm\mathrm{i}(I-X_θ^2)^{1/2}))$. For the matrix power $g(A)=A^m$, the Fourier projection of Weyl LCHM gives \[ A^m=\frac{2}π\int_0^π\text{e}^{\text{i} mθ}T_m(X_θ) \text{d}θ= \frac{2}{N}\sum_{j=0}^{N-1} \text{e}^{\text{i} mθ_j}T_m(X_{θ_j}),\quadθ_j=\frac{πj}{N},\quad \text{for every } N>m \] with Chebyshev polynomial of Hermitian $T_m(X_θ)$ and $N$ samples. The discrete formula is exact, introduces no truncation and angular quadrature error, and offers $\mathcal{O}(1)$ post-selection weights. LCHM formulas lead to new quantum eigenvalue transformation (QET) algorithms. For a degree-$d$ polynomial $p_d(A)$ on $|ψ\rangle$, our QET algorithm can achieve optimal $Θ(d)$ circuit depth and optimal $\mathcal{O}(||p_d||_{\infty}/||p_d(A)|ψ\rangle||)$ post-selection repetitions. LCHM-based QETs unify various quantum linear algebraic problems with near-optimal $\mathcal{\widetilde O}(d\log(d/ε))$ Clifford$+T$ gates, including driven ODEs (reduced to standard LCHS), iterative methods, resolvents, $\log(I+A)$, $(λI+A)^ν$, Sign and ReLU transforms, and Faber approximation on noncircular domains.

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