Quantum Convolutional Neural Networks for High Energy Physics Data Analysis

Understand

This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events.

• The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment.
• The proposed architecture demonstrates the quantum advantage of learning faster than the classical convolutional neural networks (CNNs) under a similar number of parameters.
• In addition to faster convergence, the QCNN achieves greater test accuracy compared to CNNs.