Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection
This work introduces a novel approach for data analysis in high-energy physics by leveraging quantum mechanical methods, potentially aiding in anomaly detection tasks, though it appears incremental as it adapts existing field theory methodologies to machine learning applications.
The study tackled the problem of applying quantum Hamiltonian-based models to generative modeling and anomaly detection, specifically on simulated Large Hadron Collider data, showing that such data can be represented as a mixed state and that different sample types exhibit distinct dynamical behaviors when treated as quantum many-body systems.
The Hamiltonian of an isolated quantum mechanical system determines its dynamics and physical behaviour. This study investigates the possibility of learning and utilising a system's Hamiltonian and its variational thermal state estimation for data analysis techniques. For this purpose, we employ the method of Quantum Hamiltonian-based models for the generative modelling of simulated Large Hadron Collider data and demonstrate the representability of such data as a mixed state. In a further step, we use the learned Hamiltonian for anomaly detection, showing that different sample types can form distinct dynamical behaviours once treated as a quantum many-body system. We exploit these characteristics to quantify the difference between sample types. Our findings show that the methodologies designed for field theory computations can be utilised in machine learning applications to employ theoretical approaches in data analysis techniques.