Quantum Convolutional Neural Networks for Jet Classification

Jets are an essential tool part of in nuclear and particle physics quest to in the study subatomic matter as they constitute the experimental signature of quarks and gluons. Vast amounts of data need to be processed to classify jets according to their origin. Some techniques have been devised to speed up this task while maintaining performance, when compared to traditional cut-based techniques. Some of these methods include machine learning. Quantum computing holds the promise of speeding of some of the computationally expensive tasks in physics analysis such as classification and clustering. We propose to develop a quantum algorithm based on a convolutional neural network and apply it to jet classification.