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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/215245
Energy storage and quantum simulations with optical networks
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Abstract
Quantum optical neural networks (QONN) stand out as promising variational algorithms for quantum simulations by leveraging the potential advantages of photonic circuits with respect to other hardware platforms. In this work, the basics of our proposed physically-inspired architecture of a continuousvariable
QONN and its applicability to both optimization of quantum energy storage devices and simulation of quantum field theory will be discussed. Concerning the former, the dependence of the relative energy precision on stellar rank and bipartite correlations in quantum battery systems will be analysed, whereas for the latter, a preliminary study of the Orbifold formulation of U(1) Yang-Mills theory through exact diagonalisation will be presented.
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Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2023-2024. Tutors: Federico Centrone, Paolo Stornati, Antonio Acín
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POLO RODRÍGUEZ, Beatriz. Energy storage and quantum simulations with optical networks. [consulted: 15 of June of 2026]. Available at: https://hdl.handle.net/2445/215245