Minimalist project pages for selected research works with a large ML component.
A per-particle quantum encoding scheme assigning each jet constituent to a single qubit. Demonstrated via a quantum autoencoder for anomaly detection and a variational quantum classifier for top-quark jet tagging, validated on real CMS experimental data.
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Enriches classical ML models with a quantum Fisher information view derived from variational quantum circuits, yielding richer feature representations. Demonstrates improved jet tagging at the LHC and molecular property prediction on the QM9 dataset.
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