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Publication - Dr Jianwei Wang

    Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond

    Citation

    Paesani, S, Wang, J, Santagati, R, Knauer, S, Gentile, AA, Wiebe, N, Petruzzella, M, Laing, A, Rarity, J, O'Brien, J & Thompson, M, 2017, ‘Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond’. in: 2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC): Proceedings of a meeting held 25-29 June 2017, Munich, Germany. Institute of Electrical and Electronics Engineers (IEEE)

    Abstract

    The efficient characterization and validation of the underlying model of a quantum physical system is a central challenge in the development of quantum devices and for our understanding of foundational quantum physics. However, the impossibility to efficiently predict the behaviour of complex quantum models on classical machines makes this challenge to be intractable to classical approaches. Quantum Hamiltonian Learning (QHL) [1, 2] combines the capabilities of quantum information processing and classical machine learning to allow the efficient characterisation of the model of quantum systems. In QHL the behaviour of a quantum Hamiltonian model is efficiently predicted by a quantum simulator, and the predictions are contrasted with the data obtained from the quantum system to infer the system Hamiltonian via Bayesian methods.

    Full details in the University publications repository