IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-43617-0.html
   My bibliography  Save this article

Multi-client distributed blind quantum computation with the Qline architecture

Author

Listed:
  • Beatrice Polacchi

    (Sapienza Università di Roma)

  • Dominik Leichtle

    (CNRS, Sorbonne Université)

  • Leonardo Limongi

    (Sapienza Università di Roma)

  • Gonzalo Carvacho

    (Sapienza Università di Roma)

  • Giorgio Milani

    (Sapienza Università di Roma)

  • Nicolò Spagnolo

    (Sapienza Università di Roma)

  • Marc Kaplan

    (VeriQloud)

  • Fabio Sciarrino

    (Sapienza Università di Roma)

  • Elham Kashefi

    (CNRS, Sorbonne Université
    University of Edinburgh
    National Quantum Computing Centre)

Abstract

Universal blind quantum computing allows users with minimal quantum resources to delegate a quantum computation to a remote quantum server, while keeping intrinsically hidden input, algorithm, and outcome. State-of-art experimental demonstrations of such a protocol have only involved one client. However, an increasing number of multi-party algorithms, e.g. federated machine learning, require the collaboration of multiple clients to carry out a given joint computation. In this work, we propose and experimentally demonstrate a lightweight multi-client blind quantum computation protocol based on a recently proposed linear quantum network configuration (Qline). Our protocol originality resides in three main strengths: scalability, since we eliminate the need for each client to have its own trusted source or measurement device, low-loss, by optimizing the orchestration of classical communication between each client and server through fast classical electronic control, and compatibility with distributed architectures while remaining intact even against correlated attacks of server nodes and malicious clients.

Suggested Citation

  • Beatrice Polacchi & Dominik Leichtle & Leonardo Limongi & Gonzalo Carvacho & Giorgio Milani & Nicolò Spagnolo & Marc Kaplan & Fabio Sciarrino & Elham Kashefi, 2023. "Multi-client distributed blind quantum computation with the Qline architecture," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43617-0
    DOI: 10.1038/s41467-023-43617-0
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-43617-0
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-43617-0?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. Frank Arute & Kunal Arya & Ryan Babbush & Dave Bacon & Joseph C. Bardin & Rami Barends & Rupak Biswas & Sergio Boixo & Fernando G. S. L. Brandao & David A. Buell & Brian Burkett & Yu Chen & Zijun Chen, 2019. "Quantum supremacy using a programmable superconducting processor," Nature, Nature, vol. 574(7779), pages 505-510, October.
    2. Kevin Marshall & Christian S. Jacobsen & Clemens Schäfermeier & Tobias Gehring & Christian Weedbrook & Ulrik L. Andersen, 2016. "Continuous-variable quantum computing on encrypted data," Nature Communications, Nature, vol. 7(1), pages 1-7, December.
    3. Tomoyuki Morimae & Keisuke Fujii, 2012. "Blind topological measurement-based quantum computation," Nature Communications, Nature, vol. 3(1), pages 1-6, January.
    Full references (including those not matched with items on IDEAS)

    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. Tong Liu & Shang Liu & Hekang Li & Hao Li & Kaixuan Huang & Zhongcheng Xiang & Xiaohui Song & Kai Xu & Dongning Zheng & Heng Fan, 2023. "Observation of entanglement transition of pseudo-random mixed states," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    2. Sofia Priazhkina & Samuel Palmer & Pablo Martín-Ramiro & Román Orús & Samuel Mugel & Vladimir Skavysh, 2024. "Digital Payments in Firm Networks: Theory of Adoption and Quantum Algorithm," Staff Working Papers 24-17, Bank of Canada.
    3. X. L. He & Yong Lu & D. Q. Bao & Hang Xue & W. B. Jiang & Z. Wang & A. F. Roudsari & Per Delsing & J. S. Tsai & Z. R. Lin, 2023. "Fast generation of Schrödinger cat states using a Kerr-tunable superconducting resonator," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Hu, Jie-Ru & Zhang, Zuo-Yuan & Liu, Jin-Ming, 2024. "Implementation of three-qubit Deutsch-Jozsa algorithm with pendular states of polar molecules by optimal control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    5. Huang, Fangyu & Tan, Xiaoqing & Huang, Rui & Xu, Qingshan, 2022. "Variational convolutional neural networks classifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    6. Jesús Fernández-Villaverde & Isaiah J. Hull, 2023. "Dynamic Programming on a Quantum Annealer: Solving the RBC Model," NBER Working Papers 31326, National Bureau of Economic Research, Inc.
    7. Maryam Moghimi & Herbert W. Corley, 2020. "Information Loss Due to the Data Reduction of Sample Data from Discrete Distributions," Data, MDPI, vol. 5(3), pages 1-18, September.
    8. 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.
    9. Xianchuang Pan & Yuxuan Zhou & Haolan Yuan & Lifu Nie & Weiwei Wei & Libo Zhang & Jian Li & Song Liu & Zhi Hao Jiang & Gianluigi Catelani & Ling Hu & Fei Yan & Dapeng Yu, 2022. "Engineering superconducting qubits to reduce quasiparticles and charge noise," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    10. Ducuara, Andrés F. & Susa, Cristian E. & Reina, John H., 2022. "Emergence of maximal hidden quantum correlations and its trade-off with the filtering probability in dissipative two-qubit systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    11. Nikolaos Schetakis & Davit Aghamalyan & Michael Boguslavsky & Agnieszka Rees & Marc Rakotomalala & Paul Robert Griffin, 2024. "Quantum Machine Learning for Credit Scoring," Mathematics, MDPI, vol. 12(9), pages 1-12, May.
    12. Jake Rochman & Tian Xie & John G. Bartholomew & K. C. Schwab & Andrei Faraon, 2023. "Microwave-to-optical transduction with erbium ions coupled to planar photonic and superconducting resonators," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    13. Reis, Mauricio & Oliveira, Adelcio C., 2022. "A complementary resource relation of concurrence and roughness for a two-qubit state," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P2).
    14. Jin Ming Koh & Tommy Tai & Ching Hua Lee, 2024. "Realization of higher-order topological lattices on a quantum computer," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    15. T. Brown & E. Doucet & D. Ristè & G. Ribeill & K. Cicak & J. Aumentado & R. Simmonds & L. Govia & A. Kamal & L. Ranzani, 2022. "Trade off-free entanglement stabilization in a superconducting qutrit-qubit system," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    16. Daniel Christian Lawo & Rana Abu Bakar & Abraham Cano Aguilera & Filippo Cugini & José Luis Imaña & Idelfonso Tafur Monroy & Juan Jose Vegas Olmos, 2024. "Wireless and Fiber-Based Post-Quantum-Cryptography-Secured IPsec Tunnel," Future Internet, MDPI, vol. 16(8), pages 1-22, August.
    17. Yulin Chi & Jieshan Huang & Zhanchuan Zhang & Jun Mao & Zinan Zhou & Xiaojiong Chen & Chonghao Zhai & Jueming Bao & Tianxiang Dai & Huihong Yuan & Ming Zhang & Daoxin Dai & Bo Tang & Yan Yang & Zhihua, 2022. "A programmable qudit-based quantum processor," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    18. 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.
    19. Ryan Snodgrass & Vincent Kotsubo & Scott Backhaus & Joel Ullom, 2024. "Dynamic acoustic optimization of pulse tube refrigerators for rapid cooldown," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    20. Fazal Raheman, 2022. "The Future of Cybersecurity in the Age of Quantum Computers," Future Internet, MDPI, vol. 14(11), pages 1-12, November.

    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:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43617-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.