Speaker
Description
In High Energy Physics (HEP), calorimeter outputs play an essential role in understanding low distance processes occurring during particle collisions. Due to the complexity of underlying physics, the traditional Monte-Carlo simulation is computationally expensive, and thus, the HEP community has suggested Generative Adversarial Networks (GAN) for fast simulation. Meanwhile, it has also been proposed that, in certain circumstances, simulation using GANs can itself be sped-up by using quantum GANs (qGANs).
Our work presents two advanced prototypes of qGAN to reproduce calorimeter outputs interpreted as pixelated images. The first model is called the dual-Parameterized Quantum Circuit (PQC) GAN, which consists of two PQCs sharing the role of a single classical generator. The first PQC learns the probability distribution over the images, while the second generates normalized pixel intensities of an individual image for each PQC input. Its application in HEP demonstrates that the model can reproduce a fixed number of images as well as their probability distribution for a reduced problem size and allows us to scale up to real calorimeter outputs.
On the other hand, the second prototype employs a Continuous Variable (CV) approach, which encodes quantum information in a continuous physical observable. The CV architecture has an advantage that it allows constructing a CV neural network (CVNN) similar to the structure of a classical fully connected layer. We built a simple binary classifier with the CVNNs to discriminate real classical data embedded in quantum states from random fake data. Following the successful results obtained in the CV classifier simulation, CV qGAN models are tested to generate calorimeter outputs with a reduced size, and their limitations are discussed.
