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.
Quantum graph neural networks
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A generative optimization loop using diffusion models, PINNs, and GNNs achieves 85.6% of fourth-order Qiskit fidelity at 21.8% circuit depth for transverse-field Ising model Trotter-Suzuki decomposition.
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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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Physics Guided Generative Optimization for Trotter Suzuki Decomposition
A generative optimization loop using diffusion models, PINNs, and GNNs achieves 85.6% of fourth-order Qiskit fidelity at 21.8% circuit depth for transverse-field Ising model Trotter-Suzuki decomposition.