A 4-qubit quantum predicate head in CFEN achieves 57.25% mR@100 on Visual Genome 150 long-tailed predicates versus 41.1% classical, using 96 parameters and 256x feature compression.
HQNN-FSP: A hybrid classical- quantum neural network for regression-based financial stock market prediction
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LSTM trained with a QCBM generative prior beats a classical LSTM on SSE/CSI 300 realized volatility and retains much of that edge under zero-weight inference via Drop-Prior training.
On Iris-scale hybrid quantum neural networks, ZNE, PEC, DDD, and LRE do not reliably outperform the unmitigated noisy baseline across five simulated noise channels.
Empirical scaling study finds dataset-dependent performance saturation and quantum metric trends in hybrid QNN classifiers as depth and width vary.
citing papers explorer
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QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation
A 4-qubit quantum predicate head in CFEN achieves 57.25% mR@100 on Visual Genome 150 long-tailed predicates versus 41.1% classical, using 96 parameters and 256x feature compression.
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A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference
LSTM trained with a QCBM generative prior beats a classical LSTM on SSE/CSI 300 realized volatility and retains much of that edge under zero-weight inference via Drop-Prior training.
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Robustness Evaluation of Hybrid Quantum Neural Networks under Noise Models via System-Level Error Mitigation
On Iris-scale hybrid quantum neural networks, ZNE, PEC, DDD, and LRE do not reliably outperform the unmitigated noisy baseline across five simulated noise channels.
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Scaling Laws for Hybrid Quantum Neural Networks: Depth, Width, and Quantum-Centric Diagnostics
Empirical scaling study finds dataset-dependent performance saturation and quantum metric trends in hybrid QNN classifiers as depth and width vary.