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This research advances quantum algorithms for eigenvalue transformations by leveraging contour integral representations, offering superior efficiency over existing methods for solving asymptotically stable differential equations. By combining this approach with sampling-based linear combinations of unitaries, the authors demonstrate a practical implementation requiring only three additional qubits to estimate observables. The work provides a significant theoretical and practical step forward in applying quantum computing to complex scientific computation tasks beyond standard singular value transformation frameworks.
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Contour-integral based quantum eigenvalue transformation: analysis and applications

arXiv2 min read

arXiv:2601.11959v3 Announce Type: replace Abstract: Eigenvalue transformations appear ubiquitously in scientific computation, ranging from matrix polynomials to differential equations, and are beyond the reach of the quantum singular value transformation framework. In this work, we study the efficiency of quantum algorithms based on contour integral representation for eigenvalue transformations from both theoretical and practical aspects. Theoretically, we establish a complete complexity analysis of the contour integral approach proposed in [Takahira, Ohashi, Sogabe, and Usuda. Quant. Inf. Comput., 22, 11\&12, 965--979 (2021)]. Moreover, we combine the contour integral approach and the sampling-based linear combination of unitaries to propose a quantum algorithm for estimating observables of eigenvalue transformations using only $3$ additional qubits. Practically, we design contour integral based quantum algorithms for linear ordinary differential equations, $\varphi$-functions, Fr\'echet derivatives of matrix functions, and matrix functions. In particular, we show that the contour integral algorithm can outperform all the existing quantum algorithms in the case of solving asymptotically stable differential equations.

Abstract:Eigenvalue transformations appear ubiquitously in scientific computation, ranging from matrix polynomials to differential equations, and are beyond the reach of the quantum singular value transformation framework. In this work, we study the efficiency of quantum algorithms based on contour integral representation for eigenvalue transformations from both theoretical and practical aspects. Theoretically, we establish a complete complexity analysis of the contour integral approach proposed in [Takahira, Ohashi, Sogabe, and Usuda. Quant. Inf. Comput., 22, 11\&12, 965--979 (2021)]. Moreover, we combine the contour integral approach and the sampling-based linear combination of unitaries to propose a quantum algorithm for estimating observables of eigenvalue transformations using only $3$ additional qubits. Practically, we design contour integral based quantum algorithms for linear ordinary differential equations, $\varphi$-functions, Fréchet derivatives of matrix functions, and matrix functions. In particular, we show that the contour integral algorithm can outperform all the existing quantum algorithms in the case of solving asymptotically stable differential equations.

Quantum Physics (quant-ph); Numerical Analysis (math.NA)

arXiv:2601.11959 [quant-ph]

(or arXiv:2601.11959v3 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2601.11959 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 248 words

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