A federated QLSTM model achieves near-classical accuracy on SUSY classification with under 300 parameters and 20K data points, claiming 100x efficiency gains over baselines.
Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy,
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Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics
A federated QLSTM model achieves near-classical accuracy on SUSY classification with under 300 parameters and 20K data points, claiming 100x efficiency gains over baselines.