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.
FL-QDSNNs: Federated learning with quantum dynamic spiking neural networks,
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QSNN agent in Q-SpiRL framework achieves up to 99% success rate with efficient paths in 20x20 to 40x40 grid worlds with static and dynamic obstacles, outperforming tabular Q-learning, MLP, SNN, and QMLP baselines under unified evaluation.
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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
QSNN agent in Q-SpiRL framework achieves up to 99% success rate with efficient paths in 20x20 to 40x40 grid worlds with static and dynamic obstacles, outperforming tabular Q-learning, MLP, SNN, and QMLP baselines under unified evaluation.