University Library
  • Login
A gateway to Melbourne's research publications
Minerva Access is the University's Institutional Repository. It aims to collect, preserve, and showcase the intellectual output of staff and students of the University of Melbourne for a global audience.
View Item 
  • Minerva Access
  • Science
  • School of Physics
  • School of Physics - Research Publications
  • View Item
  • Minerva Access
  • Science
  • School of Physics
  • School of Physics - Research Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

    Framework for atomic-level characterisation of quantum computer arrays by machine learning

    Thumbnail
    Download
    Published version (2.081Mb)

    Citations
    Scopus
    Web of Science
    Altmetric
    2
    2
    Author
    Usman, M; Wong, YZ; Hill, CD; Hollenberg, LCL
    Date
    2020-03-16
    Source Title
    npj Computational Materials
    Publisher
    Nature Research (part of Springer Nature)
    University of Melbourne Author/s
    Usman, Muhammad; Wong, Yi Zheng; Hill, Charles; Hollenberg, Lloyd
    Affiliation
    School of Physics
    Computing and Information Systems
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Usman, M., Wong, Y. Z., Hill, C. D. & Hollenberg, L. C. L. (2020). Framework for atomic-level characterisation of quantum computer arrays by machine learning. npj Computational Materials, 6 (1), https://doi.org/10.1038/s41524-020-0282-0.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/252012
    DOI
    10.1038/s41524-020-0282-0
    Abstract
    Atomic-level qubits in silicon are attractive candidates for large-scale quantum computing; however, their quantum properties and controllability are sensitive to details such as the number of donor atoms comprising a qubit and their precise location. This work combines machine learning techniques with million-atom simulations of scanning tunnelling microscopic (STM) images of dopants to formulate a theoretical framework capable of determining the number of dopants at a particular qubit location and their positions with exact lattice site precision. A convolutional neural network (CNN) was trained on 100,000 simulated STM images, acquiring a characterisation fidelity (number and absolute donor positions) of >98% over a set of 17,600 test images including planar and blurring noise commensurate with experimental measurements. The formalism is based on a systematic symmetry analysis and feature-detection processing of the STM images to optimise the computational efficiency. The technique is demonstrated for qubits formed by single and pairs of closely spaced donor atoms, with the potential to generalise it for larger donor clusters. The method established here will enable a high-precision post-fabrication characterisation of dopant qubits in silicon, with high-throughput potentially alleviating the requirements on the level of resources required for quantum-based characterisation, which will otherwise be a challenge in the context of large qubit arrays for universal quantum computing.

    Export Reference in RIS Format     

    Endnote

    • Click on "Export Reference in RIS Format" and choose "open with... Endnote".

    Refworks

    • Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References


    Collections
    • Minerva Elements Records [53102]
    • Computing and Information Systems - Research Publications [1584]
    • School of Physics - Research Publications [1059]
    Minerva AccessDepositing Your Work (for University of Melbourne Staff and Students)NewsFAQs

    BrowseCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    My AccountLoginRegister
    StatisticsMost Popular ItemsStatistics by CountryMost Popular Authors