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Experimental quantum Hamiltonian learning

Research output: Contribution to journalLetter

Original languageEnglish
Pages (from-to)551-555
Number of pages5
JournalNature Physics
Volume13
Issue number6
Early online date13 Mar 2017
DOIs
DateAccepted/In press - 16 Feb 2017
DateE-pub ahead of print - 13 Mar 2017
DatePublished (current) - Jun 2017

Abstract

The efficient characterization of quantum systems, the verification of the operations of quantum devices and the validation of underpinning physical models, are central challenges for quantum technologies and fundamental physics. The computational cost of such studies could be improved by machine learning enhanced by quantum simulators. Here we interface two different quantum systems through a classical channel—a silicon-photonics quantum simulator and an electron spin in a diamond nitrogen–vacancy centre—and use the former to learn the Hamiltonian of the latter via Bayesian inference. We learn the salient Hamiltonian parameter with an uncertainty of approximately 10-5. Furthermore, an observed saturation in the learning algorithm suggests deficiencies in the underlying Hamiltonian model, which we exploit to further improve the model. We implement an interactive version of the protocol and experimentally show its ability to characterize the operation of the quantum photonic device.

    Research areas

  • Quantum information processing, Quantum optics, silicon photonics, nv centers

    Structured keywords

  • Bristol Quantum Information Institute

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Springer Nature at http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys4074.html . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 37 MB, PDF document

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