Quantum convolutional neural networks for jet images classification

TL;DR

This paper explores the use of quantum convolutional neural networks for classifying jet images in high-energy physics, showing potential performance improvements over classical CNNs especially with optimized circuit configurations.

Contribution

It introduces a quantum CNN approach for jet image classification and compares its performance with classical CNNs, highlighting the benefits of quantum models with fewer parameters.

Findings

QCNN outperforms classical CNN in certain configurations

Lower parameter QCNN circuits can achieve better results

DEA-optimized circuits improve quantum model performance

Abstract

Recently, interest in quantum computing has significantly increased, driven by its potential advantages over classical techniques. Quantum machine learning (QML) exemplifies one of the important quantum computing applications that are expected to surpass classical machine learning in a wide range of instances. This paper addresses the performance of QML in the context of high-energy physics (HEP). As an example, we focus on the top-quark tagging, for which classical convolutional neural networks (CNNs) have been effective but fall short in accuracy when dealing with highly energetic jet images. In this paper, we use a quantum convolutional neural network (QCNN) for this task and compare its performance with CNN using a classical noiseless simulator. We compare various setups for the QCNN, varying the convolutional circuit, type of encoding, loss function, and batch sizes. For every quantum setup, we design a similar setup to the corresponding classical model for a fair comparison. Our results indicate that QCNN with proper setups tend to perform better than their CNN counterparts, particularly when the convolution block has a lower number of parameters. For the higher parameter regime, the QCNN circuit was adjusted according to the dimensional expressivity analysis (DEA) to lower the parameter count while preserving its optimal structure. The DEA circuit demonstrated improved results over the comparable classical CNN model.