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arXiv 2011.07145hep-thcond-mat.dis-nncs.LGnlin.CDquant-ph

Chaos and Complexity from Quantum Neural Network: A study with Diffusion Metric in Machine Learning

National Institute of Science Education and Research, Bhubaneswar(布巴内斯瓦尔国家科学教育与研究学院);Homi Bhabha National Institute(霍米·巴巴国家研究所);Max Planck Institute for Gravitational Physics (Albert Einstein Institute)(马克斯·普朗克引力物理研究所(阿尔伯特·爱因斯坦研究所));Jadavpur University(贾达普大学);Mississippi State University(密西西比州立大学)

Sayantan Choudhury, Ankan Dutta, Debisree Ray

英文摘要:

In this work, our prime objective is to study the phenomena of quantum chaos and complexity in the machine learning dynamics of Quantum Neural Network (QNN). A Parameterized Quantum Circuits (PQCs) in the hybrid quantum-classical framework is introduced as a universal function approximator to perform optimization with Stochastic Gradient Descent (SGD). We employ a statistical and differential geometric approach to study the learning theory of QNN. The evolution of parametrized unitary operators is correlated with the trajectory of parameters in the Diffusion metric. We establish the parametrized version of Quantum Complexity and Quantum Chaos in terms of physically relevant quantities, which are not only essential in determining the stability, but also essential in providing a very significant lower bound to the generalization capability of QNN. We explicitly prove that when the system executes limit cycles or oscillations in the phase space, the generalization capability of QNN is maximized. Finally, we have determined the generalization capability bound on the variance of parameters of the QNN in a steady state condition using Cauchy Schwartz Inequality.

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