Quantum GANs for 𝒕𝒕̅𝑯(𝒃𝒃̅) Process Data Generation

In this report, we present the Deep Learning generative model GAN for the Higgs bosont 𝒕𝒕̅𝑯(𝒃𝒃̅) process data generation. Initially, a classical GAN model is considered, with Convolutional layers, Batch Normalization layers, and a Leaky ReLU activation function. The GAN aims to simulate the Higgs process precisely, capturing the crucial features in each b-jet produced. Two b-jets were considered in this work, each possessing four features that were resized to fit the Neural Network training process, where a relatively decent Wasserstein distance was obtained. Subsequently, a Quantum GAN model was considered, where the Quantum Circuit consisted of Gaussian gates as a continuous variable architecture per the nature of the dataset constraint. Xanadu's both PennyLane and Strawberry Fields Python libraries were used on a continuous variable quantum neural networkbased, where obtaining comparable results with the classical benchmark was intended on the simulators, considering a smaller dataset with fewer features.