Hybrid LSTM-QCBM model outperforms classical LSTM on SSE Composite and CSI 300 volatility forecasting and supports quantum-assisted training followed by fully classical inference.
HQNN-FSP: A hybrid classical-quantum neural network for regression-based financial stock market prediction
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Empirical scaling study finds dataset-dependent performance saturation and quantum metric trends in hybrid QNN classifiers as depth and width vary.
Simulations show hybrid quantum neural networks on Iris data degrade under depolarizing and amplitude-damping noise while phase-flip and phase-damping noise are less damaging, with ZNE, DDD, LRE, and PEC providing limited mitigation that depends on noise type and strength.
citing papers explorer
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A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines
Hybrid LSTM-QCBM model outperforms classical LSTM on SSE Composite and CSI 300 volatility forecasting and supports quantum-assisted training followed by fully classical inference.
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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.
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Robustness Evaluation of Hybrid Quantum Neural Networks under Noise Models via System-Level Error Mitigation
Simulations show hybrid quantum neural networks on Iris data degrade under depolarizing and amplitude-damping noise while phase-flip and phase-damping noise are less damaging, with ZNE, DDD, LRE, and PEC providing limited mitigation that depends on noise type and strength.