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, which we solve using an adaptive quantum-annealing eigensolver (AQAE) and its classical equivalent. We identify two key parameters in the success of the AQAE for solving Helmholtz problems: the system condition number and the integrated control errors (ICEs) in the quantum hardware. Our results show that a large system condition number implies a finer discretization grid for the AQAE to converge, leading to a variable overhead, and that the AQAE is either tolerant or not with respect to ICEs depending on the GEVP. Finally, we establish lower bounds on the annealing time, narrowing the possibility of a quantum advantage for solving Helmholtz problems.

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• Adiabatic quantum optimization
• Finite-element method
• Simulated annealing