Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection

• URL: http://arxiv.org/abs/2211.03803v3
• Date: Tue, 28 Nov 2023 19:21:04 GMT
• Title: Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection
• Authors: Jack Y. Araz and Michael Spannowsky
• Abstract summary: This study investigates the possibility of learning and utilising a system's Hamiltonian for data analysis techniques. We employ the method of Quantum Hamiltonian-based models for the generative modelling of simulated Large Hadron Collider data. In a further step, we use the learned Hamiltonian for anomaly detection, showing that different sample types can form distinct dynamical behaviours.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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