Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection

Abstract

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.

arXiv comment14 pages, 7 figures. Accepted version for publication

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Cited by in corpus (5)

• Jet Discrimination with Quantum Complete Graph Neural Network
• State preparation of lattice field theories using quantum optimal control
• Non-resonant Anomaly Detection with Background Extrapolation
• Generative Invertible Quantum Neural Networks
• Exploring thermal equilibria of the Fermi-Hubbard model with variational quantum algorithms