Searching for new physics in the era of Deep Learning and Quantum Supremacy
PT-CERN Call 2021/1
Felipe Ferreira de Freitas
Universidade de Aveiro
CIDMA - Centro de Investigação em Matemática e Aplicações da Universidade de Aveiro
Quantum Computing (QC) leverages the quantum properties of subatomic matter to enable algorithms faster than those possible on a regular computer. Quantum Computers have become increasingly practical in recent years, with some small-scale machines becoming available for public use. This dissertation proposal outlines the challenge of designing quantum algorithms for machine learning and optimization applied to High Energy Physics (HEP) event classification. Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classiﬁcation. The main goal of this thesis proposal is to develop Quantum Machine Learning (QML) approaches, focusing on Variational Quantum Circuits (VQC) to find patterns on the data collected by particle collision events and evaluate the performance of the event classification using both simulators and quantum computing devices. We are particularly interested in distinguishing New Physics (NP) signatures that would point us towards a viable Beyond the Standard Model (BSM) scenario. For this, we intend to utilize QML in combination with self-thought learning (SSL) techniques to quickly achieve a neural network capable of classifying background vs BSM events for different scenarios, namely Leptoquark interactions, a multi-Higgs doublet model with exotic decays and a model with a exotic graviton. In these BSM scenarios there would be lepton-quark currents, two lepton decays from the same scalar vertex with the same charge value, and highly energetic photons from jets emanating from a Kaluza-Klein resonance. We expect the ML apparatus to be able to identify all of these scenarios.