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Deep Neural Networks (DNNs) come into the limelight in High Energy Physics(HEP) in order to manipulate the increasing amount of data encountered in thenext generation of accelerators. Recently, the HEP community has suggestedGenerative Adversarial Networks (GANs) to replace traditional time-consumingGeant4 simulations based on the Monte Carlo method. In parallel with advancesin deep learning, intriguing studies have been conducted in the last decade onquantum computing, including the Quantum GAN model suggested by IBM. However,this model is limited in learning a probability distribution over discretevariables, while we initially aim to reproduce a distribution over continuousvariables in HEP. We introduce and analyze a new prototype of quantum GAN(qGAN) employed in continuous-variable (CV) quantum computing, which encodesquantum information in a continuous physical observable. Two CV qGAN modelswith a quantum and a classical discriminator have been tested to reproducecalorimeter outputs in a reduced size, and their advantages and limitations arediscussed.

Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors