Quantum convolutional autoencoders are adapted for reconstruction-based anomaly detection on time-series data, with a bottleneck architecture suggested to outperform hierarchical ones on an exoplanet dataset.
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Quantum Convolutional Autoencoders for Reconstruction-Based Anomaly Detection
Quantum convolutional autoencoders are adapted for reconstruction-based anomaly detection on time-series data, with a bottleneck architecture suggested to outperform hierarchical ones on an exoplanet dataset.