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arXiv:2512.22665 · IEEE International Conference on Quantum Control, Computing and Learning (qCCL)

Variational quantum algorithm for solving Helmholtz problems with high order finite elements

Arnaud Rémi, François Damanet, Christophe Geuzaine

quant-phSubmitted Dec 27, 2025• Announced Dec 30, 2025• 0 citations
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Abstract

Discretizing Helmholtz problems via finite elements yields linear systems whose efficient solution remains a major challenge for classical computation. In this paper, we investigate how variational quantum algorithms could address this challenge. We first show that, for regular meshes, a block encoding of the operators $A$ and $A^\dagger A$ arising from the high-order finite element discretisation of Helmholtz problems can be designed, resulting in a quantum circuit of depth $\mathcal{O}(p^3\mathrm{poly}\log(Np))$ with $N$ the number of elements and $p$ the order of the finite elements. Then we apply our algorithm to a one-dimensional Helmholtz problem with Dirichlet and Neumann boundary conditions for various wavenumbers.

Comments
7 pages, 4 figures

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Discretizing Helmholtz problems via finite elements yields linear systems whose efficient solution remains a major challenge for classical computation. In this paper, we investigate how variational quantum algorithms could address this challenge. We first show that, for regular meshes, a block encoding of the operators AAA and A†AA^\dagger AA†A arising from the high-order finite element discretisation of Helmholtz problems can be designed, resulting in a quantum circuit of depth O(p3polylog⁡(Np))\mathcal{O}(p^3\mathrm{poly}\log(Np))O(p3polylog(Np)) with NNN the number of elements and ppp the order of the finite elements. Then we apply our algorithm to a one-dimensional Helmholtz problem with Dirichlet and Neumann boundary conditions for various wavenumbers.
Simulation of physical systems is one of the most promising use cases of future digital quantum computers. In this work we systematically analyze the quantum circuit complexities of block encoding the discretized elliptic operators that arise extensively in numerical simulations for partial differential equations, including high-dimensional instances for many-body simulations. When restricted to rectangular domains with separable boundary conditions, we provide explicit circuits to block encode the many-body Laplacian with separable periodic, Dirichlet, Neumann, and Robin boundary conditions, using standard discretization techniques from low-order finite difference methods. To obtain high-precision, we introduce a scheme based on periodic extensions to solve Dirichlet and Neumann boundary value problems using a high-order finite difference method, with only a constant increase in total circuit depth and subnormalization factor. We then present a scheme to implement block encodings of differential operators acting on more arbitrary domains, inspired by Cartesian immersed boundary methods. We then block encode the many-body convective operator, which describes interacting particles experiencing a force generated by a pair-wise potential given as an inverse power law of the interparticle distance. This work provides concrete recipes that are readily translated into quantum circuits, with depth logarithmic in the total Hilbert space dimension, that block encode operators arising broadly in applications involving the quantum simulation of quantum and classical many-body mechanics.
Solving Helmholtz problems using finite elements leads to the resolution of a linear system which is challenging to solve for classical computers. In this paper, we investigate how quantum annealers could address this challenge. We first express the linear system arising from the Helmholtz problem as a generalized eigenvalue problem (gEVP). The obtained gEVP is mapped into quadratic unconstrained binary optimization problems (QUBOs) which we solve using an adaptive quantum annealing eigensolver (AQAE) and its classical equivalent. We identify two key parameters in the success of AQAE for solving Helmholtz problems: the system condition number and the integrated control errors (ICE) in the quantum hardware. Our results show that a large system condition number implies a finer discretization grid for AQAE to converge, leading to a variable overhead, and that AQAE is either tolerant or not with respect to ICE depending on the gEVP. Finally, we establish lower bounds on the annealing time, narrowing the possibility of a quantum advantage for solving Helmholtz problems.
The finite element method (FEM) is a cornerstone numerical technique for solving partial differential equations (PDEs). Here, we present Qu-FEM\textbf{Qu-FEM}Qu-FEM, a fault-tolerant era quantum algorithm for the finite element method. In contrast to other quantum PDE solvers, Qu-FEM preserves the geometric flexibility of FEM by introducing two new primitives, the unit of interaction and the local-to-global indicator matrix, which enable the assembly of global finite element arrays with a constant-size linear combination of unitaries. We study the modified Poisson equation as an elliptic problem of interest, and provide explicit circuits for Qu-FEM in Cartesian domains. For problems with constant coefficients, our algorithm admits block-encodings of global arrays that require only O~(d2p2n)\tilde{\mathcal{O}}\left(d^2 p^2 n\right)O~(d2p2n) Clifford+TTT gates for ddd-dimensional, order-ppp tensor product elements on grids with 2n2^n2n degrees of freedom in each dimension, where nnn is the number of qubits representing the N=2nN=2^nN=2n discrete grid points. For problems with spatially varying coefficients, we perform numerical integration directly on the quantum computer to assemble global arrays and force vectors. Dirichlet boundary conditions are enforced via the method of Lagrange multipliers, eliminating the need to modify the block-encodings that emerge from the assembly procedure. This work presents a framework for extending the geometric flexibility of quantum PDE solvers while preserving the possibility of a quantum advantage.
Solving a Poisson equation is generally reduced to solving a linear system with a coefficient matrix AAA of entries aij,i,j=1,2,...,n{a}_{ij}, i,j=1,2,...,naij​,i,j=1,2,...,n, from the discretized Poisson equation. Although the variational quantum algorithms are promising algorithms to solve the discretized Poisson equation, they generally require that AAA be decomposed into a sum of O[poly(log2n)]O[\text{poly}({\text{log}}_{2}n)]O[poly(log2​n)] simple operators in order to evaluate efficiently the loss function. A tensor product decomposition of AAA with 2log2n+12{\text{log}}_{2}n+12log2​n+1 terms has been explored in previous works. In this paper, based on the decomposition of sparse Hamiltonians we greatly reduce the number of terms. We first write the loss function in terms of the operator ${\ensuremath{\sigma}}_{x}\ensuremath{\bigotimes}A$ with ${\ensuremath{\sigma}}_{x}$ denoting the standard Pauli operator. Then for the one-dimensional Poisson equations with different boundary conditions and for the ddd-dimensional Poisson equations with Dirichlet boundary conditions, we decompose ${\ensuremath{\sigma}}_{x}\ensuremath{\bigotimes}A$ into a sum of at most 7 and (4d+1)(4d+1)(4d+1) Hermitian, one-sparse, and self-inverse operators, respectively. We design explicitly the quantum circuits to evaluate efficiently the loss function. The decomposition method and the design of quantum circuits can also be easily extended to linear systems with Hermitian and sparse coefficient matrices satisfying ai,i+c=ac{a}_{i,i+c}={a}_{c}ai,i+c​=ac​ for $c=0,1,...,n\ensuremath{-}1$ and $i=0,\ensuremath{\cdots},n\ensuremath{-}1\ensuremath{-}c$.
Solving differential equations is one of the most computationally expensive problems in classical computing, occupying the vast majority of high-performance computing resources devoted towards practical applications in various fields of science and engineering. Despite recent progress made in the field of quantum computing and quantum algorithms, its end-to-end application towards practical realization still remains unattainable. In this article, we tackle one of the primary obstacles towards this ultimate objective, specifically the encoding of matrices derived via finite difference method solving Poisson partial differential equations in simple boundary-value problems. To that end, we propose a novel methodology called block-diagonalization, which provides a common decomposition form for our matrices, and similarly a common procedure for block-encoding these matrices inside a unitary operator of a quantum circuit. The depth of these circuits is double-logarithmic in the matrix size, which is an exponential improvement over existing quantum methods and a superexponential improvement over existing classical methods. These improvements come at the price of a constant multiplicative overhead on the number of qubits and the number of gates. Combined with quantum linear solver algorithms, we can utilize these quantum circuits to produce a quantum state representation of the solution to the Poisson partial differential equations and their boundary-value problems.
This manuscript explores a variational quantum formulation for nonlinear elasticity problems arising from hyperelastic material models. The approach leverages the potential energy structure of hyperelasticity and employs a hybrid quantum classical framework in which the energy functional is evaluated using parameterized quantum circuits and optimized through classical routines. To enable a hybrid (classical quantum implementation), polynomial approximations of the nonlinear terms in strain energy density are introduced, yielding a representation compatible with variational quantum algorithms. The methodology is demonstrated on a special case of the NeoHookean material model in a one dimensional setting using finite element discretizations with first and second order shape functions and nonhomogeneous boundary conditions. Numerical experiments investigate the influence of the polynomial approximation order on the accuracy and efficiency of the proposed approach, illustrating its feasibility for near-term quantum devices.