Hybrid quantum neural networks improve macro F1-score by up to 3.7% over classical baselines on two public blood cell datasets while remaining robust on noisy quantum hardware.
Computational advantage in hybrid quantum neural networks: Myth or reality?
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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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Enhancing Blood Cells Classification using Hybrid Quantum Neural Networks
Hybrid quantum neural networks improve macro F1-score by up to 3.7% over classical baselines on two public blood cell datasets while remaining robust on noisy quantum hardware.
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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.