Dataset for numerical results and figures in "Qudit extension of parameterized IQP circuits: A generative quantum machine learning approach to integer data"

This dataset contains numerical data underlying the plots and results of the associated manuscript. In the files there is a Python notebook to easily read the files and reproduce the manuscript figures. -Calorimeter data: It consists of six Python pickle files evaluated at three parameter settings, including training/validation datasets and model outputs. The data include energies, training and validation representations, quantization errors, loss evolution, MMD metrics, covariance and correlation matrices, and trained parameters. -Potts model data: It consists of two Python json files. For the target distribution and the results from the IQP circuit training. These files enable full reproducibility of the reported figures and quantitative analyses in the manuscript.