Quantum-assisted GAN networks for particle shower simulation

This work explores the applicability of quantum machine learning (QML) methods, especially quantum-classical associative adversarial networks, to tune the training complexity for generative models for particle shower simulation. These QML methods will allow for fast and reliable simulations of particle showers in calorimeters in subatomic physics experiments if successful. Our approach will train generative layers that implement quantum versions of generative adversarial network (GAN) models. These implementations have been developed, but implementing these models on near-term hardware will suffer from the input-output size problem since quantum computing hardware is currently too small to handle the input data from HEP datasets directly. For this reason, we consider an approach for incorporating near-term quantum hardware into deep learning models in which a quantum model is trained and deployed on quantum hardware and used to implement a portion (e.g., a layer of a deep neural network) of the overall deep learning model.