Introduces a unified benchmarking methodology for quantum transfer learning in visual classification tasks, finding that no single method dominates and performance varies with dataset, encoding, and circuit design.
Qnn-vrcs: A quantum neural network for vehicle road cooperation systems
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5representative citing papers
SPATE encodes data via spike trains mapped to quantum phases, yielding stronger feature representations than angle or amplitude encoding on datasets like Blobs and Moons.
A quantum-enhanced spiking Q-network is reported to outperform classical, spiking, and quantum-dense baselines in small grid-world navigation, with gains that are small relative to the reported error bars.
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.
Demonstrates FLOPs-aware neural architecture search for hybrid quantum-classical neural networks to produce accurate yet computationally efficient models suitable for NISQ hardware.
citing papers explorer
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Towards Fair Benchmarking of Quantum Transfer Learning for Visual Classification
Introduces a unified benchmarking methodology for quantum transfer learning in visual classification tasks, finding that no single method dominates and performance varies with dataset, encoding, and circuit design.
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SPATE: Spiking-Phase Adaptive Temporal Encoding for Quantum Machine Learning
SPATE encodes data via spike trains mapped to quantum phases, yielding stronger feature representations than angle or amplitude encoding on datasets like Blobs and Moons.
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Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation
A quantum-enhanced spiking Q-network is reported to outperform classical, spiking, and quantum-dense baselines in small grid-world navigation, with gains that are small relative to the reported error bars.
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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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Hybrid Quantum-Classical Neural Architecture Search
Demonstrates FLOPs-aware neural architecture search for hybrid quantum-classical neural networks to produce accurate yet computationally efficient models suitable for NISQ hardware.