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Quantum Techniques in Machine Learning

6–11 Dec 2026
Stellenbosch University
Africa/Johannesburg timezone

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    Contact

    • qtml2026@nithecs.ac.za

    Details for Francesco Petruccione

    NITheCS

    Author in the following contributions

    • Comparing Persistent and Bottleneck Quantum Convolutional Autoencoders for Reconstruction-Based Anomaly Detection
    • A quantum-inspired spiking variational autoencoder for robust anomaly detection at the LHC
    • A continuous-variable photonic memristor as a reinforcement learner
    • Automated Circuit Design of Quantum Convolutional Neural Networks for Medical Image Classification
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