Quantum Learning for Accelerated Nuclear Data Analysis and Simulation

A crucial element of any analysis in nuclear physics involves the simulation of the physical processes and interactions taking place at these facilities to develop new theories and models to explain experimental data and characterize background, study detector response, and plan for detector upgrades. These simulations are often computationally intensive, taking up a significant fraction of the computational resources available to nuclear physicists. Recently, alternative methods for detector simulation and data analysis tasks have been explored, like machine learning applications and quantum information science (QIS). QIS is a rapidly developing field focused on understanding the analysis, processing, and transmission of information using quantum mechanical principles and computational techniques. QIS can address the conventional computing gap associated with NP-related problems, specifically those computational tasks that challenge CPUs and GPUs, such as efficient and accurate event generators. In addition, quantum computing offers unique advantages over classical computing in machine learning and optimization. Nonetheless, adapting these new technologies to the analysis of NP data requires developing domain-specific tools and algorithms, such as quantum machine learning (QML) algorithms tailored to NP applications. In this work, we introduce a quantum generative model trained to simulate events that resemble the training data statistics at the vertex level.