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Quantum computational advantage with a programmable photonic processor

Author

Listed:
  • Lars S. Madsen

    (Xanadu)

  • Fabian Laudenbach

    (Xanadu)

  • Mohsen Falamarzi. Askarani

    (Xanadu)

  • Fabien Rortais

    (Xanadu)

  • Trevor Vincent

    (Xanadu)

  • Jacob F. F. Bulmer

    (Xanadu)

  • Filippo M. Miatto

    (Xanadu)

  • Leonhard Neuhaus

    (Xanadu)

  • Lukas G. Helt

    (Xanadu)

  • Matthew J. Collins

    (Xanadu)

  • Adriana E. Lita

    (National Institute of Standards and Technology)

  • Thomas Gerrits

    (National Institute of Standards and Technology)

  • Sae Woo Nam

    (National Institute of Standards and Technology)

  • Varun D. Vaidya

    (Xanadu)

  • Matteo Menotti

    (Xanadu)

  • Ish Dhand

    (Xanadu)

  • Zachary Vernon

    (Xanadu)

  • Nicolás Quesada

    (Xanadu)

  • Jonathan Lavoie

    (Xanadu)

Abstract

A quantum computer attains computational advantage when outperforming the best classical computers running the best-known algorithms on well-defined tasks. No photonic machine offering programmability over all its quantum gates has demonstrated quantum computational advantage: previous machines1,2 were largely restricted to static gate sequences. Earlier photonic demonstrations were also vulnerable to spoofing3, in which classical heuristics produce samples, without direct simulation, lying closer to the ideal distribution than do samples from the quantum hardware. Here we report quantum computational advantage using Borealis, a photonic processor offering dynamic programmability on all gates implemented. We carry out Gaussian boson sampling4 (GBS) on 216 squeezed modes entangled with three-dimensional connectivity5, using a time-multiplexed and photon-number-resolving architecture. On average, it would take more than 9,000 years for the best available algorithms and supercomputers to produce, using exact methods, a single sample from the programmed distribution, whereas Borealis requires only 36 μs. This runtime advantage is over 50 million times as extreme as that reported from earlier photonic machines. Ours constitutes a very large GBS experiment, registering events with up to 219 photons and a mean photon number of 125. This work is a critical milestone on the path to a practical quantum computer, validating key technological features of photonics as a platform for this goal.

Suggested Citation

  • Lars S. Madsen & Fabian Laudenbach & Mohsen Falamarzi. Askarani & Fabien Rortais & Trevor Vincent & Jacob F. F. Bulmer & Filippo M. Miatto & Leonhard Neuhaus & Lukas G. Helt & Matthew J. Collins & Adr, 2022. "Quantum computational advantage with a programmable photonic processor," Nature, Nature, vol. 606(7912), pages 75-81, June.
  • Handle: RePEc:nat:nature:v:606:y:2022:i:7912:d:10.1038_s41586-022-04725-x
    DOI: 10.1038/s41586-022-04725-x
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    Citations

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    Cited by:

    1. 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.
    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. Francesco Bova & Avi Goldfarb & Roger G. Melko, 2023. "Quantum Economic Advantage," Management Science, INFORMS, vol. 69(2), pages 1116-1126, February.
    4. Mark Dong & Julia M. Boyle & Kevin J. Palm & Matthew Zimmermann & Alex Witte & Andrew J. Leenheer & Daniel Dominguez & Gerald Gilbert & Matt Eichenfield & Dirk Englund, 2023. "Synchronous micromechanically resonant programmable photonic circuits," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    5. Ojas Parekh, 2023. "Synergies Between Operations Research and Quantum Information Science," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 266-273, March.
    6. Skavysh, Vladimir & Priazhkina, Sofia & Guala, Diego & Bromley, Thomas R., 2023. "Quantum monte carlo for economics: Stress testing and macroeconomic deep learning," Journal of Economic Dynamics and Control, Elsevier, vol. 153(C).
    7. Saket Kaushal & A. Aadhi & Anthony Roberge & Roberto Morandotti & Raman Kashyap & José Azaña, 2023. "All-fibre phase filters with 1-GHz resolution for high-speed passive optical logic processing," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    8. Vladimir Skavysh & Sofia Priazhkina & Diego Guala & Thomas Bromley, 2022. "Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning," Staff Working Papers 22-29, Bank of Canada.
    9. Dominik D. Bühler & Matthias Weiß & Antonio Crespo-Poveda & Emeline D. S. Nysten & Jonathan J. Finley & Kai Müller & Paulo V. Santos & Mauricio M. Lima & Hubert J. Krenner, 2022. "On-chip generation and dynamic piezo-optomechanical rotation of single photons," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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