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This paper introduces the Lindbladian homotopy analysis method (LHAM), a novel algorithm designed to solve nonlinear partial differential equations on quantum computers while mitigating the curse of dimensionality. By reformulating nonlinear dynamics into a linear homogeneous system, LHAM achieves logarithmic growth in Hilbert space dimension compared to polynomial scaling in traditional approaches like Carleman linearization. The method is validated on complex physical systems such as Burgers' equation and reduced magnetohydrodynamics, marking a significant step forward for quantum scientific computing applications.
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Lindbladian Homotopy Analysis Method to Solve Nonlinear Partial Differential Equations

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2604.18924v3 Announce Type: replace-cross Abstract: Quantum scientific computing is to solve engineering and science problems such as simulation and optimization on quantum computers. Solving ordinary and partial differential equations (PDEs) is essential in simulations. However, existing quantum approaches to solve nonlinear PDEs suffer from the issues of curse of dimensionality and convergence during the linearization process. In this paper, a Lindbladian homotopy analysis method (LHAM) is proposed as a quantum differential equation solver to simulate nonlinear and nonunitary dynamics. The original nonlinear problem is first converted to a recursive sequence of linear PDEs with the homotopy analysis method and reformulated as a higher-dimensional lower block triangular linear homogeneous autonomous system. The solution is then embedded in the density matrix and obtained through the Lindblad dynamics simulation. Compared to other methods such as Carleman linearization and the Koopman-von Neumann approach where the dimension of Hilbert space increases polynomially with the inverse of truncation error, the Hilbert space dimension in LHAM increases only logarithmically. LHAM is demonstrated with nonlinear PDEs including Burgers' equation and reduced magnetohydrodynamics equations.

Abstract:Quantum scientific computing is to solve engineering and science problems such as simulation and optimization on quantum computers. Solving ordinary and partial differential equations (PDEs) is essential in simulations. However, existing quantum approaches to solve nonlinear PDEs suffer from the issues of curse of dimensionality and convergence during the linearization process. In this paper, a Lindbladian homotopy analysis method (LHAM) is proposed as a quantum differential equation solver to simulate nonlinear and nonunitary dynamics. The original nonlinear problem is first converted to a recursive sequence of linear PDEs with the homotopy analysis method and reformulated as a higher-dimensional lower block triangular linear homogeneous autonomous system. The solution is then embedded in the density matrix and obtained through the Lindblad dynamics simulation. Compared to other methods such as Carleman linearization and the Koopman-von Neumann approach where the dimension of Hilbert space increases polynomially with the inverse of truncation error, the Hilbert space dimension in LHAM increases only logarithmically. LHAM is demonstrated with nonlinear PDEs including Burgers' equation and reduced magnetohydrodynamics equations.

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

arXiv:2604.18924 [math.NA]

(or arXiv:2604.18924v3 [math.NA] for this version)

https://doi.org/10.48550/arXiv.2604.18924 Focus to learn more arXiv-issued DOI via DataCite

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