Crafting Generative Models & Unraveling High-Energy Physics with Parameterized Circuits

The advent of Noisy Intermediate-Scale Quantum (NISQ) computing has spotlighted the significance of parameterized quantum circuits (PQCs) as essential tools in quantum technology. Particularly in the realm of quantum generative models, PQCs have shown promise in encapsulating complex data distributions, reproducing the statistics of the training data, and detecting anomalous instances.

This seminar will weave a narrative around the confluence of PQCs in both quantum machine learning and the intricate world of high energy physics. Through a fusion of case studies and in-depth discussions, I will highlight the promise and potential of PQCs, standing at the crossroads of data-driven innovation and fundamental scientific discovery.

Dr. Delgado is a scientist at the Physics Division at Oak Ridge National Laboratory, known for her adeptness in high energy physics. Her passion has always gravitated towards understanding the universe's fundamental particles and interactions. Transitioning from the world of particle accelerators to quantum computing, Delgado currently works on integrating quantum machine learning into high energy physics research. By fusing these two groundbreaking domains, Delgado aspires to uncover novel insights and methodologies that could redefine our understanding of the cosmos. Outside the lab, Delgado frequently collaborates with international research teams and is an active advocate for diversity and inclusion in the STEM fields.