Learning nitrogen-vacancy electron spin dynamics on a silicon quantum photonic simulator

Jianwei Wang, Stefano Paesani, Raffaele Santagati, Sebastian Knauer, Antonio Andreas Gentile, Nathan Wiebe, Maurangelo Petruzzella, Anthony Laing, John Rarity, Jeremy O'Brien, Mark Thompson

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

We report the learning of electron spin Hamiltonian in a diamond nitrogen-vacancy centre using a silicon-photonics quantum simulator with classical machine learning, showing a new ability of efficient characterisation and verification of quantum devices/systems.
Original languageEnglish
Title of host publication2017 Conference on Lasers and Electro-Optics (CLEO)
Subtitle of host publicationProceedings of a meeting held 16-18 May 2017, San Jose, California, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9781943580279
DOIs
Publication statusPublished - 14 May 2017
Event2017 Conference on Lasers and Electro-Optics, CLEO - San Jose Convention Center, San Jose, CA, United States
Duration: 14 May 201719 May 2017

Conference

Conference2017 Conference on Lasers and Electro-Optics, CLEO
Abbreviated titleCLEO 2017
CountryUnited States
CitySan Jose, CA
Period14/05/1719/05/17

Structured keywords

  • Bristol Quantum Information Institute

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  • Projects

    Fabricating a photonic quantum computer.

    O'Brien, J. L.

    1/04/1331/03/18

    Project: Research

    Cite this

    Wang, J., Paesani, S., Santagati, R., Knauer, S., Gentile, A. A., Wiebe, N., Petruzzella, M., Laing, A., Rarity, J., O'Brien, J., & Thompson, M. (2017). Learning nitrogen-vacancy electron spin dynamics on a silicon quantum photonic simulator. In 2017 Conference on Lasers and Electro-Optics (CLEO): Proceedings of a meeting held 16-18 May 2017, San Jose, California, USA [FTu1F.5] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1364/CLEO_QELS.2017.FTu1F.5