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.
Variational quantum algorithm based on lagrange polynomial encoding to solve differential equations ,
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Tensor-rank quantum and quantum-inspired PINNs solve the Merton HJB PDE with lower error and fewer parameters than classical fully connected PINNs.
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Trainable Quantum Spectral Models for Partial Differential Equations
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.
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Learning PDEs for Portfolio Optimization with Quantum Physics-Informed Neural Networks
Tensor-rank quantum and quantum-inspired PINNs solve the Merton HJB PDE with lower error and fewer parameters than classical fully connected PINNs.