Towards analyzing large graphs with quantum annealing

Hannu Reittu (Corresponding author), Tomi Räty, Lasse Leskelä, Hannu Rummukainen, Tomi Räty

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

    Abstract

    The use of quantum computing in graph community detection and regularity checking related to Szemerédi's Regularity Lemma (SRL) are demonstrated with D-Wave Systems' quantum annealer and simulations. We demonstrate the capability of quantum computing in solving hard problems relevant to big data. A new community detection algorithm based on SRL is also introduced and tested.

    Original languageEnglish
    Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
    EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages2457-2464
    Number of pages8
    ISBN (Electronic)978-1-7281-0858-2
    ISBN (Print)978-1-7281-0859-9
    DOIs
    Publication statusPublished - 2019
    MoE publication typeA4 Article in a conference publication
    EventThe 2019 IEEE International Conference on Big Data - Los Angeles, United States
    Duration: 9 Dec 201912 Dec 2019
    http://bigdataieee.org/BigData2019/

    Conference

    ConferenceThe 2019 IEEE International Conference on Big Data
    Abbreviated titleIEEE BigData 2019
    CountryUnited States
    CityLos Angeles
    Period9/12/1912/12/19
    Internet address

    Keywords

    • Quantum computing
    • quantum annealing
    • graph communitiy detection

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  • Cite this

    Reittu, H., Räty, T., Leskelä, L., Rummukainen, H., & Räty, T. (2019). Towards analyzing large graphs with quantum annealing. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 2457-2464). [9006174] IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/BigData47090.2019.9006174