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.
The power of quantum neural networks
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Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
Meta-learning with 24 classical complexity metrics predicts the optimal quantum encoding circuit among 9 candidates with up to 85.7% top-3 accuracy.
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
Survey of quantum feature encoding families with a cost-expressivity-robustness taxonomy, closed-form NISQ bounds, and a five-regime decision framework that recommends shallow angle encodings when gate error rate p is at or above 10^-3.
The paper introduces Recursive QLSTM via metacore recursion, numerically tests variants on sequence lengths, and offers theoretical arguments for better temporal propagation.
citing papers explorer
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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.
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Quantum Injection Pathways for Implicit Graph Neural Networks
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
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Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning
Meta-learning with 24 classical complexity metrics predicts the optimal quantum encoding circuit among 9 candidates with up to 85.7% top-3 accuracy.
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Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
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Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines
Survey of quantum feature encoding families with a cost-expressivity-robustness taxonomy, closed-form NISQ bounds, and a five-regime decision framework that recommends shallow angle encodings when gate error rate p is at or above 10^-3.
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Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
The paper introduces Recursive QLSTM via metacore recursion, numerically tests variants on sequence lengths, and offers theoretical arguments for better temporal propagation.
- Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets