Classical and Quantum Machine Learning for Anomaly Detection at the CMS Experiment and Beyond

This thesis presents two novel methodologies for anomaly detection in particle collision events, utilising both classical and quantum machine learning techniques. The first approach employs an unsupervised, data-driven variational autoencoder (VAE), trained on collision data recorded by the CMS detector at the Large Hadron Collider during Run 2. No significant excess above the Standard Model (SM) expectation is observed. This method is subsequently used to derive exclusion limits on a large number of exotic signal models derived from Beyond the Standard Model (BSM) physics. The second approach introduces a novel quantum machine learning framework, named 1P1Q, for encoding jet kinematic features onto two-level quantum bits (qubits). For both the anomaly detection and supervised classification tasks, the 1P1Q method is demonstrated to achieve signal identification and separation performance comparable to or surpassing current state-of-the-art classical machine learning algorithms.