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.
Zoufal, Generative quantum machine learning, arXiv preprint arXiv:2111.12738 (2021)
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Quantum convolutional autoencoders are adapted for reconstruction-based anomaly detection on time-series data, with a bottleneck architecture suggested to outperform hierarchical ones on an exoplanet dataset.
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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 Convolutional Autoencoders for Reconstruction-Based Anomaly Detection
Quantum convolutional autoencoders are adapted for reconstruction-based anomaly detection on time-series data, with a bottleneck architecture suggested to outperform hierarchical ones on an exoplanet dataset.