Speaker
Description
Quantum computing is a rapidly emerging technology with potential applications in particle physics. From the direct simulation of quantum field theories on quantum hardware to the acceleration of computationally demanding components of data analysis, the interface between quantum information science and high-energy physics has become an active and expanding area of research. Potential applications include the quantum simulation of lattice gauge theories and real-time quantum dynamics, as well as hybrid quantum–classical algorithms for optimisation, sampling, and data-driven analysis in collider and phenomenological studies. Although current devices remain in the noisy intermediate-scale quantum (NISQ) era and are limited in scale and noise resilience, this period provides an important testing ground for developing algorithms for future large-scale quantum machines.
This talk will provide a high-level and accessible introduction to quantum computing, with particular emphasis on quantum machine learning. I will discuss two complementary perspectives. First, the use of parameterised quantum circuits as trainable machine learning models, with potential applications to data analysis; and second, the use of classical machine learning techniques to design and control quantum systems, for example, through quantum optimal control methods aimed at improving quantum simulation. I will outline potential advantages, discuss current limitations and realistic near-term expectations, and highlight recent contributions from our group in these areas.