Quantum Convolutional Neural Networks for High-Energy Physics Analysis at the LHC
Published:
Image credits: Quantum convolutional neural networks
Synopsis
Determining whether an image of a jet particle corresponds to signals or background signals is one of the many challenges faced in High Energy Physics. CNNs have been effective against jet particle images as well for classification purposes. Quantum computing is promising in this regard and as the QML field is evolving, this project aims to understand and implement QCNN and gain some enhancement.
The goal of this study is to show the capabilities of QML especially QCNN for classifying the HEP image datasets. QCNN can be completely quantum or can be a hybrid with classical. The aim is to implement both. We will use quantum variational classification instead of the final FC classical layers in the quantum setting. This will give more depth about the quantum power that can be used in the near term future.