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Partial Differential Equations in Applied Mathematics

Quantum-assisted hλ-adaptive finite element method

R. H. Drebotiy, H. А. Shynkarenko

Published Feb 7, 2025• 1 citation
Publisher

Abstract

Quantum computing is a rapidly advancing field, driven by the potential advantages derived from the unique properties of quantum entanglement. In particular, the exponential speedup of certain carefully designed algorithms, compared to their classical counterparts, promises to significantly enhance the numerical solution of a wide range of problems. This paper investigates the integration of quantum computing with the finite element method, focusing on singularly perturbed advection-diffusion-reaction problems. We introduce a novel finite element scheme that combines classical and quantum algorithms. In this approach, the primary mesh adaptation loop is managed by a classical computer, while a specific stabilization procedure is executed on a quantum computer. This procedure leverages the Harrow-Hassidim-Lloyd algorithm in conjunction with the swap test to estimate the value of a linear functional, which constitutes a substantial portion of the computational workload. We demonstrate that this hybrid approach effectively eliminates parasitic oscillations in the finite element approximation, even at the early stages of the adaptation process. This leads to a significant improvement in the quality of intermediate finite element solutions. As a result, our scheme offers more efficient feedback with reduced computational costs for researchers using the method to investigate physical phenomena. To support the scheme, we prove special explicit a posteriori error estimates. Possible benefits of the proposed finite element scheme are analyzed using the numerical comparison with the typical adaptive scheme.

DOI
10.1016/j.padiff.2025.101120

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Quantum computing is a rapidly advancing field, driven by the potential advantages derived from the unique properties of quantum entanglement. In particular, the exponential speedup of certain carefully designed algorithms, compared to their classical counterparts, promises to significantly enhance the numerical solution of a wide range of problems. This paper investigates the integration of quantum computing with the finite element method, focusing on singularly perturbed advection-diffusion-reaction problems. We introduce a novel finite element scheme that combines classical and quantum algorithms. In this approach, the primary mesh adaptation loop is managed by a classical computer, while a specific stabilization procedure is executed on a quantum computer. This procedure leverages the Harrow-Hassidim-Lloyd algorithm in conjunction with the swap test to estimate the value of a linear functional, which constitutes a substantial portion of the computational workload. We demonstrate that this hybrid approach effectively eliminates parasitic oscillations in the finite element approximation, even at the early stages of the adaptation process. This leads to a significant improvement in the quality of intermediate finite element solutions. As a result, our scheme offers more efficient feedback with reduced computational costs for researchers using the method to investigate physical phenomena. To support the scheme, we prove special explicit a posteriori error estimates. Possible benefits of the proposed finite element scheme are analyzed using the numerical comparison with the typical adaptive scheme.
Quantum simulation of electromagnetic (EM) structures is still in its infancy. In this paper, we investigate the possibility of applying quantum computing to solve the finite element equations for EM problems. Specifically, we propose to leverage a milestone in quantum computing, i.e., the Harrow/Hassidim/Lloyd (HHL) algorithm, to solve the finite element equations in the EM domain. To do this, we first present a systematic method to reformulate the finite element equations into quantum computation format and apply the HHL algorithm to solve the new equations. Taking advantage of the special properties of finite element matrices in EM problems, we then devise a stepwise algorithm to efficiently determine the suitable values for the hyperparameters of HHL. The proposed method can in theory solve the EM finite element equations in logarithmic time relative to the size of the finite element matrix. This is exponentially faster than that achievable by classical computation as the matrix size increases. A two-dimensional EM example is used to illustrate how the proposed method can be used to find the solutions to EM problems through quantum computation.
Algorithms that promise to leverage resources of quantum computers efficiently to accelerate the finite element method have emerged. However, the finite element method is usually incorporated into a high-level numerical scheme which allows the adaptive refinement of the mesh on which the solution is approximated. In this work, we propose to extend adaptive mesh refinement to the quantum formalism, and apply our method to the solution of Maxwell's equations. An important step in this procedure is the computation of error estimators, which guide the refinement. By using block-encoding, we propose a way to compute these estimators with quantum circuits. We present first numerical experiments on a 2D geometry.
In the context of investigating the potential application of quantum computers to the finite element method, we propose a specific approach for solving the two-dimensional parabolic advection-diffusion-reaction boundary value problem by transforming it into a set of corresponding one-dimensional problems. This method employs specialized operator splitting, resulting in a recurrent fractional-step scheme for time integration. While this scheme offers significant parallelization potential, the main goal is to map the original large elliptic 2D problem arising at each time step into a set of smaller 1D problems of the same kind. This transformation, combined with recently proposed quantum-assisted stabilization schemes, allows us to pass smaller linear systems to a quantum linear solver, making them suitable for near-term quantum computers. We combine the proposed algorithm with the finite element method to solve the resulting one-dimensional problems, although these problems can also be addressed using other discretization techniques, such as the finite volume or finite difference methods.
This manuscript presents the Quantum Finite Element Method (Q-FEM) developed for use in noisy intermediate-scale quantum (NISQ) computers and employs the variational quantum linear solver (VQLS) algorithm. The proposed method leverages the classical FEM procedure to perform the unitary decomposition of the stiffness matrix and employs generator functions to design explicit quantum circuits corresponding to the unitaries. Q-FEM keeps the structure of the finite element discretization intact allowing for the use of variable element lengths and material coefficients in FEM discretization. The proposed method is tested on a steady-state heat equation discretized using linear and quadratic shape functions. Numerical verification studies are performed on the IBM QISKIT simulator and it is demonstrated that Q-FEM is effective in converging to the correct solution for a variety of problems and model discretizations, including with different element lengths, variable coefficients, and different boundary conditions. The formalism developed herein is general and can be extended to problems with higher dimensions. However, numerical examples also demonstrate that the number of parameters for the variational ansatz scale exponentially with the number of qubits, and increases the odds of convergence. Moreover, the deterioration of system conditioning with problem size results in barren plateaus and convergence difficulties.
Quantum computing is a potential approach for addressing computational challenges in solving electromagnetic problems. A quantum method, called the Harrow/Hassidim /Lloyd (HHL) algorithm has recently been applied to solve the equations from finite-element-method (FEM) formulation of electromagnetic problems. Here we address a further step in this direction. Since quantum state vectors always have unit norm, the solution vector from HHL is only proportional to the final electromagnetic solutions. The magnitude of the final electromagnetic solution vector remains unavailable from HHL In this paper, a new quantum formulation is proposed to find the magnitude of the final electromagnetic solutions. The proposed method can be used to scale the HHL results to the correct magnitude of electromagnetic solutions. In this way, the proposed method is a further advance in making the quantum computing more effective in solving electromagnetic problems.
Numerical simulations of physical systems are found in many industries, as they currently play a crucial role in product development. There are many numerical methods for solving differential equations that describe the underlying physics behind the mathematical models in the simulation, among which, the finite element method (FEM) is one of the most commonly used. Although in many applications the FEM seems to provide an acceptable solution to the problem, there are still many complex real-life processes that can be challenging to simulate numerically due to their complexity and large size. Recently, there has been a shift in research towards efficiently applying quantum algorithms in finite element analysis (FEA), as the potential and speedup that they could offer have been shown, but little to no effort has been made towards the applicability and cost efficiency of these algorithms in real-world quantum devices. In this paper, we propose a cost-efficient method for applying quantum algorithms in FEA for industrial problems post-processed by classical algorithms in order to address the limitations of available quantum hardware and their cost when accessing them through different cloud-based services. We carry this out by approximating the solution of the initially large system with a suitable quantum algorithm and using the obtained solutions to generate a set of reduced-order models (ROMs) that are much smaller in complexity and size than the original model. This allows the simulation of the original model with different parameter sets and excitations to be run efficiently on classical computers without having the need to access quantum subroutines again. This way, we have reduced the usage of quantum hardware (and thus the development cost) while still taking advantage of its quantum speedup.