Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors
This work addresses the need for faster simulations in high-energy physics, but it is incremental as it adapts existing quantum GAN concepts to a continuous-variable setting.
The authors tackled the problem of simulating high-energy physics detector outputs by developing a quantum generative adversarial network (qGAN) in a continuous-variable architecture, achieving results that reproduce calorimeter outputs in a reduced size and discussing their advantages and limitations.
Deep Neural Networks (DNNs) come into the limelight in High Energy Physics (HEP) in order to manipulate the increasing amount of data encountered in the next generation of accelerators. Recently, the HEP community has suggested Generative Adversarial Networks (GANs) to replace traditional time-consuming Geant4 simulations based on the Monte Carlo method. In parallel with advances in deep learning, intriguing studies have been conducted in the last decade on quantum computing, including the Quantum GAN model suggested by IBM. However, this model is limited in learning a probability distribution over discrete variables, while we initially aim to reproduce a distribution over continuous variables in HEP. We introduce and analyze a new prototype of quantum GAN (qGAN) employed in continuous-variable (CV) quantum computing, which encodes quantum information in a continuous physical observable. Two CV qGAN models with a quantum and a classical discriminator have been tested to reproduce calorimeter outputs in a reduced size, and their advantages and limitations are discussed.