An approximate quantum Hamiltonian identification algorithm using a Taylor expansion of the matrix exponential function

Yuanlong Wang, Daoyi Dong, Ian R. Petersen

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Citations (Scopus)

    Abstract

    An approximate quantum Hamiltonian identification algorithm is presented with the assumption that the system initial state and observation matrix can be set appropriately. We sample the system with a fixed period and using the sampled data we estimate the Hamiltonian based on a Taylor expansion of the matrix exponential function. We prove the estimation error is linear in the variance of the additive Gaussian noise. We also propose a heuristic formula to find the order of magnitude of the optimal sampling period. Two numerical examples are presented to validate the theoretical results.

    Original languageEnglish
    Title of host publication2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages5523-5528
    Number of pages6
    ISBN (Electronic)9781509028733
    DOIs
    Publication statusPublished - 28 Jun 2017
    Event56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia
    Duration: 12 Dec 201715 Dec 2017

    Publication series

    Name2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
    Volume2018-January

    Conference

    Conference56th IEEE Annual Conference on Decision and Control, CDC 2017
    Country/TerritoryAustralia
    CityMelbourne
    Period12/12/1715/12/17

    Fingerprint

    Dive into the research topics of 'An approximate quantum Hamiltonian identification algorithm using a Taylor expansion of the matrix exponential function'. Together they form a unique fingerprint.

    Cite this