An Implementation of the Finite Element Method in Hybrid Classical/Quantum Computers

TL;DR

This paper introduces the Quantum Finite Element Method (Q-FEM) for noisy quantum computers, integrating classical FEM with quantum algorithms to solve PDEs, and demonstrates its effectiveness and challenges through numerical tests.

Contribution

It develops a hybrid classical-quantum FEM approach using variational quantum algorithms, enabling PDE solutions on NISQ devices with a scalable formalism.

Findings

Q-FEM converges to correct solutions for various discretizations

Number of variational parameters scales exponentially with qubits

System conditioning issues affect convergence in larger problems

Abstract

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.