grid points on a laptop. A method for encoding arbitrary analytical functions into diagonal Matrix Product Operators is employed to prepare and evolve a Matrix Product State (MPS) encoding the solution. Since the method relies on the Quantum Fourier Transform, which has been shown to generate small entanglement when applied to arbitrary MPSs, simulating the evolution of initial conditions with sufficiently low bond dimensions to high accuracy becomes highly efficient, up to the cost of Trotterized propagation and sampling of the wavefunction. We conclude by discussing possible extensions of the approach for carrying out Tensor Network simulations of other partial differential equations such as Maxwell's equations.

DOI: 10.48550/arxiv.2504.11181

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10^{13}

N

signal on two paired

n

-qubit registers and decompose the overall map into a non-unitary exponential Damping Transform followed by a Quantum Fourier Transform, both compressed in a single matrix-product operator. This decomposition admits strong MPO compression to low bond dimension resulting in significant acceleration. We demonstrate simulations up to

N=2^{30}

input data points, with up to

2^{60}

output data points, and quantify how bond dimension controls runtime and accuracy, including precise and efficient pole identification.

10^3

relative to Trotterized MPS evolution, yielding orders of magnitude reduction in the time-to-solution under realistic levels of parallelisation. Finally, we numerically observe that MPS TE-PAI is substantially more robust against severe bond-dimension truncation than product formulas, potentially making it useful for the simulation of strongly correlated systems where truncation is necessary in practice. We also demonstrate that the approach can be used naturally in combination with existing time evolution algorithms, effectively extending their time depth via parallelisation.