Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.
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Hybrid quantum-classical physics-informed neural networks reach accurate solutions to nonlinear PDEs in substantially fewer training epochs than purely classical networks, with larger gains on complex problems.
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
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Learning PDEs for Portfolio Optimization with Quantum Physics-Informed Neural Networks
Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.
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Quantum-Enhanced Convergence of Physics-Informed Neural Networks
Hybrid quantum-classical physics-informed neural networks reach accurate solutions to nonlinear PDEs in substantially fewer training epochs than purely classical networks, with larger gains on complex problems.
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A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.