  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.
