Trainable quantum spectral models with an intermediate parameterized mixer (ε ≈ 0.5) outperform standard variational quantum circuits for PDEs by learning in spectral representation, with HHL-inspired architectures showing fastest convergence.
arXiv preprint arXiv:2503.12244 , year=
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Trainable Quantum Spectral Models for Partial Differential Equations
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