Distance-4 bivariate bicycle codes plus an interleaved feed-forward decoder give a 29-physical-qubit encoding of a 4-qubit QCNN that is more noise-resilient than the bare circuit at 0.1% error rates.
Quantum machine learning
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2representative citing papers
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.
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Low-Overhead Error-Corrected QCNNs Using Bivariate Bicycle Codes
Distance-4 bivariate bicycle codes plus an interleaved feed-forward decoder give a 29-physical-qubit encoding of a 4-qubit QCNN that is more noise-resilient than the bare circuit at 0.1% error rates.
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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.