QCPIKAN is a quantum-classical physics-informed KAN that claims exponential high-frequency error convergence and superior accuracy over prior QCPINNs on single-phase, transport, and two-phase seepage PDEs.
F., Stefanou, P., & Pons, J
5 Pith papers cite this work, alongside 30 external citations. Polarity classification is still indexing.
5
Pith papers citing it
30
external citations · OpenAlex
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 5roles
background 2polarities
background 2representative citing papers
HQPINNs reduce relative L2 error by roughly fourfold on Burgers' equation and fivefold on Allen-Cahn equation versus classical PINNs, with smoother training and largest gains in stiff regimes.
Tensor-rank quantum and quantum-inspired PINNs solve the Merton HJB PDE with lower error and fewer parameters than classical fully connected PINNs.
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.
citing papers explorer
-
Quantum-classical physics-informed Kolmogorov-Arnold networks for PDEs
QCPIKAN is a quantum-classical physics-informed KAN that claims exponential high-frequency error convergence and superior accuracy over prior QCPINNs on single-phase, transport, and two-phase seepage PDEs.
-
Hybrid quantum-classical physics-informed neural networks for solving nonlinear PDEs: when and where hybridization is effective?
HQPINNs reduce relative L2 error by roughly fourfold on Burgers' equation and fivefold on Allen-Cahn equation versus classical PINNs, with smoother training and largest gains in stiff regimes.
-
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.
-
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.
-
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.