A dual GNN predictor evaluates hardware-aware quantum circuit graphs to select task-specific kernels that balance expressivity and NISQ constraints, outperforming baselines on classification benchmarks.
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Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
TDVP-MPS simulations of Rydberg atom chains mitigate exponential concentration in QELM, yielding competitive MNIST accuracy via controlled entanglement and disorder without requiring exact quantum dynamics.
citing papers explorer
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Hardware-Aware Quantum Kernel Design Based on Graph Neural Networks
A dual GNN predictor evaluates hardware-aware quantum circuit graphs to select task-specific kernels that balance expressivity and NISQ constraints, outperforming baselines on classification benchmarks.
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Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
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Harnessing Quantum Dynamics for Robust and Scalable Quantum Extreme Learning Machines
TDVP-MPS simulations of Rydberg atom chains mitigate exponential concentration in QELM, yielding competitive MNIST accuracy via controlled entanglement and disorder without requiring exact quantum dynamics.