Machine Learning Applied in Reconstruction of Unitary Matrix for Quantum Computation

H. Zhang, H. Cai, D. Paesani, R. Santagati, A. Laing, L. C. Kwek, A. Q. Liu

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

Abstract

Optimal method are applied in characterizing and reconstructing designed unitary matrices on linear optical circuit. The scheme is based on the measurement of single-photon and two-photon statistics using coherent beams.

Original languageEnglish
Title of host publication2019 Conference on Lasers and Electro-Optics, CLEO 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9781943580576
DOIs
Publication statusPublished - 1 May 2019
Event2019 Conference on Lasers and Electro-Optics, CLEO 2019 - San Jose, United States
Duration: 5 May 201910 May 2019

Publication series

Name2019 Conference on Lasers and Electro-Optics, CLEO 2019 - Proceedings

Conference

Conference2019 Conference on Lasers and Electro-Optics, CLEO 2019
CountryUnited States
CitySan Jose
Period5/05/1910/05/19

Keywords

  • Optical design
  • Photonics
  • Genetic Algorithms
  • Structural beams
  • quantum computing
  • quantum gates

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  • Cite this

    Zhang, H., Cai, H., Paesani, D., Santagati, R., Laing, A., Kwek, L. C., & Liu, A. Q. (2019). Machine Learning Applied in Reconstruction of Unitary Matrix for Quantum Computation. In 2019 Conference on Lasers and Electro-Optics, CLEO 2019 - Proceedings [8749941] (2019 Conference on Lasers and Electro-Optics, CLEO 2019 - Proceedings). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.23919/CLEO.2019.8749941