Speaker
Juan Carlos Criado
(Durham University)
Description
Quantum annealing provides an optimization framework with the potential to outperform classical algorithms in finding the global minimum of non-convex functions. The availability of quantum annealers with thousands of qubits makes it possible today to tackle real-world problems using this technology. In this talk, I will review the quantum annealing paradigm and its use in the minimization of general functions. I will then discuss some of the applications of this method in high-energy physics, including training neural networks for classification, and fitting effective field theories to experimental data.
Author
Juan Carlos Criado
(Durham University)
