Quantum convolutional neural networks for jet images classification
Quantum Convolutional Neural Networks (QCNNs) were tested to classify high-energy physics jet images for top-quark tagging, aiming to beat classical CNNs while managing excessive parameters. The study found QCNNs generally outperformed classical models in noise-free conditions. A key finding was Dimensional Expressivity Analysis (DEA), which successfully reduced trainable parameters by identifying redundancies without sacrificing accuracy.