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.
Quantum vs. classical machine learning: A benchmark study for financial prediction
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6representative 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.
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.
GAT-QNN uses a two-stage genetic algorithm to train macroCircuits and select efficient microCircuits for hybrid quantum neural networks, reporting 22-23% accuracy gains on 4-class MNIST across backends.
Quantum machine learning models do not surpass classical baselines in prediction performance, policy stability, or training time, though they may help filter noise and control false positives.
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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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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GAT-QNN: Genetic Algorithm-Based Training of Hybrid Quantum Neural Networks
GAT-QNN uses a two-stage genetic algorithm to train macroCircuits and select efficient microCircuits for hybrid quantum neural networks, reporting 22-23% accuracy gains on 4-class MNIST across backends.
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Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
Quantum machine learning models do not surpass classical baselines in prediction performance, policy stability, or training time, though they may help filter noise and control false positives.