论文标题
用于生成建模和异常检测
Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection
论文作者
论文摘要
孤立的量子机械系统的哈密顿量决定了其动力学和身体行为。这项研究调查了学习和利用系统的哈密顿量及其对数据分析技术的变异热状态估计的可能性。为此，我们采用了基于量子哈密顿的模型的方法来模拟大型强子撞机数据的生成建模，并证明了此类数据等混合状态的能力。在进一步的一步中，我们使用学到的哈密顿量进行异常检测，这表明，一旦被视为量子多体系统，不同的样本类型可以形成独特的动力学行为。我们利用这些特征来量化样本类型之间的差异。我们的发现表明，用于现场理论计算设计的方法可以用于机器学习应用程序中，以在数据分析技术中采用理论方法。
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.
