Quantum Wave Simulation with Sources and Loss Functions

• URL: http://arxiv.org/abs/2411.17630v1
• Date: Tue, 26 Nov 2024 17:42:55 GMT
• Title: Quantum Wave Simulation with Sources and Loss Functions
• Authors: Cyrill Bösch, Malte Schade, Giacomo Aloisi, Andreas Fichtner,
• Abstract summary: This framework encompasses a broad class of wave equations, including the acoustic wave equation, Maxwells equations, and the elastic wave equation. We show that subspace energies can be extracted and wave fields compared through an $l$-loss function. We argue that this quartic speed-up is optimal for time domain solutions, as the Hamiltonian of the discretized wave equations has local couplings.
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• Abstract: We present a quantum algorithmic framework for simulating linear, anti-Hermitian (lossless) wave equations in heterogeneous, anisotropic, but time-independent media. This framework encompasses a broad class of wave equations, including the acoustic wave equation, Maxwells equations, and the elastic wave equation. Our formulation is compatible with standard numerical discretization schemes and allows for efficient implementation of source terms for diverse time- and space-dependent sources. Furthermore, we demonstrate that subspace energies can be extracted and wave fields compared through an $l_2$-loss function, achieving optimal precision scaling with the number of samples taken. Additionally, we introduce techniques for incorporating boundary conditions and linear constraints that preserve the anti-Hermitian nature of the equations. Leveraging the Hamiltonian simulation algorithm, our framework achieves a quartic speed-up over classical solvers in 3D simulations, under conditions of sufficiently global measurements and compactly supported sources and initial conditions. We argue that this quartic speed-up is optimal for time domain solutions, as the Hamiltonian of the discretized wave equations has local couplings. In summary, our framework provides a versatile approach for simulating wave equations on quantum computers, offering substantial speed-ups over state-of-the-art classical methods.

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