IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0206704.html
   My bibliography  Save this article

Validating quantum-classical programming models with tensor network simulations

Author

Listed:
  • Alexander McCaskey
  • Eugene Dumitrescu
  • Mengsu Chen
  • Dmitry Lyakh
  • Travis Humble

Abstract

The exploration of hybrid quantum-classical algorithms and programming models on noisy near-term quantum hardware has begun. As hybrid programs scale towards classical intractability, validation and benchmarking are critical to understanding the utility of the hybrid computational model. In this paper, we demonstrate a newly developed quantum circuit simulator based on tensor network theory that enables intermediate-scale verification and validation of hybrid quantum-classical computing frameworks and programming models. We present our tensor-network quantum virtual machine (TNQVM) simulator which stores a multi-qubit wavefunction in a compressed (factorized) form as a matrix product state, thus enabling single-node simulations of larger qubit registers, as compared to brute-force state-vector simulators. Our simulator is designed to be extensible in both the tensor network form and the classical hardware used to run the simulation (multicore, GPU, distributed). The extensibility of the TNQVM simulator with respect to the simulation hardware type is achieved via a pluggable interface for different numerical backends (e.g., ITensor and ExaTENSOR numerical libraries). We demonstrate the utility of our TNQVM quantum circuit simulator through the verification of randomized quantum circuits and the variational quantum eigensolver algorithm, both expressed within the eXtreme-scale ACCelerator (XACC) programming model.

Suggested Citation

  • Alexander McCaskey & Eugene Dumitrescu & Mengsu Chen & Dmitry Lyakh & Travis Humble, 2018. "Validating quantum-classical programming models with tensor network simulations," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-19, December.
  • Handle: RePEc:plo:pone00:0206704
    DOI: 10.1371/journal.pone.0206704
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0206704
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0206704&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0206704?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Alberto Peruzzo & Jarrod McClean & Peter Shadbolt & Man-Hong Yung & Xiao-Qi Zhou & Peter J. Love & Alán Aspuru-Guzik & Jeremy L. O’Brien, 2014. "A variational eigenvalue solver on a photonic quantum processor," Nature Communications, Nature, vol. 5(1), pages 1-7, September.
    2. Abhinav Kandala & Antonio Mezzacapo & Kristan Temme & Maika Takita & Markus Brink & Jerry M. Chow & Jay M. Gambetta, 2017. "Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets," Nature, Nature, vol. 549(7671), pages 242-246, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Olawale Ayoade & Pablo Rivas & Javier Orduz, 2022. "Artificial Intelligence Computing at the Quantum Level," Data, MDPI, vol. 7(3), pages 1-16, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. He, Zhimin & Deng, Maijie & Zheng, Shenggen & Li, Lvzhou & Situ, Haozhen, 2023. "GSQAS: Graph Self-supervised Quantum Architecture Search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    2. Sitan Chen & Jordan Cotler & Hsin-Yuan Huang & Jerry Li, 2023. "The complexity of NISQ," Nature Communications, Nature, vol. 14(1), pages 1-6, December.
    3. Samson Wang & Enrico Fontana & M. Cerezo & Kunal Sharma & Akira Sone & Lukasz Cincio & Patrick J. Coles, 2021. "Noise-induced barren plateaus in variational quantum algorithms," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    5. Abha Naik & Esra Yeniaras & Gerhard Hellstern & Grishma Prasad & Sanjay Kumar Lalta Prasad Vishwakarma, 2023. "From Portfolio Optimization to Quantum Blockchain and Security: A Systematic Review of Quantum Computing in Finance," Papers 2307.01155, arXiv.org.
    6. Samuel Fern'andez-Lorenzo & Diego Porras & Juan Jos'e Garc'ia-Ripoll, 2020. "Hybrid quantum-classical optimization for financial index tracking," Papers 2008.12050, arXiv.org, revised Oct 2021.
    7. Elies Gil-Fuster & Jens Eisert & Carlos Bravo-Prieto, 2024. "Understanding quantum machine learning also requires rethinking generalization," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. Eric R. Anschuetz & Bobak T. Kiani, 2022. "Quantum variational algorithms are swamped with traps," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    9. F. H. B. Somhorst & R. Meer & M. Correa Anguita & R. Schadow & H. J. Snijders & M. Goede & B. Kassenberg & P. Venderbosch & C. Taballione & J. P. Epping & H. H. Vlekkert & J. Timmerhuis & J. F. F. Bul, 2023. "Quantum simulation of thermodynamics in an integrated quantum photonic processor," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    10. Junyu Liu & Minzhao Liu & Jin-Peng Liu & Ziyu Ye & Yunfei Wang & Yuri Alexeev & Jens Eisert & Liang Jiang, 2024. "Towards provably efficient quantum algorithms for large-scale machine-learning models," Nature Communications, Nature, vol. 15(1), pages 1-6, December.
    11. Mario E Rivero-Angeles, 2021. "Quantum-based wireless sensor networks: A review and open questions," International Journal of Distributed Sensor Networks, , vol. 17(10), pages 15501477211, October.
    12. Sofiene Jerbi & Lukas J. Fiderer & Hendrik Poulsen Nautrup & Jonas M. Kübler & Hans J. Briegel & Vedran Dunjko, 2023. "Quantum machine learning beyond kernel methods," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    13. Dylan Herman & Cody Googin & Xiaoyuan Liu & Alexey Galda & Ilya Safro & Yue Sun & Marco Pistoia & Yuri Alexeev, 2022. "A Survey of Quantum Computing for Finance," Papers 2201.02773, arXiv.org, revised Jun 2022.
    14. Manuel S. Rudolph & Jacob Miller & Danial Motlagh & Jing Chen & Atithi Acharya & Alejandro Perdomo-Ortiz, 2023. "Synergistic pretraining of parametrized quantum circuits via tensor networks," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    15. Martin Vesely, 2023. "Finding the Optimal Currency Composition of Foreign Exchange Reserves with a Quantum Computer," Working Papers 2023/1, Czech National Bank.
    16. Camille Grange & Michael Poss & Eric Bourreau, 2023. "An introduction to variational quantum algorithms for combinatorial optimization problems," 4OR, Springer, vol. 21(3), pages 363-403, September.
    17. Giacomo Torlai & Christopher J. Wood & Atithi Acharya & Giuseppe Carleo & Juan Carrasquilla & Leandro Aolita, 2023. "Quantum process tomography with unsupervised learning and tensor networks," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    18. Daniel J. Egger & Claudio Gambella & Jakub Marecek & Scott McFaddin & Martin Mevissen & Rudy Raymond & Andrea Simonetto & Stefan Woerner & Elena Yndurain, 2020. "Quantum Computing for Finance: State of the Art and Future Prospects," Papers 2006.14510, arXiv.org, revised Jan 2021.
    19. E Schuyler Fried & Nicolas P D Sawaya & Yudong Cao & Ian D Kivlichan & Jhonathan Romero & Alán Aspuru-Guzik, 2018. "qTorch: The quantum tensor contraction handler," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-20, December.
    20. Antoine Jacquier & Oleksiy Kondratyev & Gordon Lee & Mugad Oumgari, 2023. "Quantum Computing for Financial Mathematics," Papers 2311.06621, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0206704. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.