Cited by 567Quantum Computing Algorithms and ArchitectureQuantum Information and CryptographyPhotonic and Optical Devices

Quantum circuits with many photons on a programmable nanophotonic chip

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Arrazola, J. M., Bergholm, V., Brádler, K., Bromley, T. R., Collins, M. J., Dhand, I., Fumagalli, A., Gerrits, T. +31 more·2021·Nature

Abstract

Growing interest in quantum computing for practical applications has led to a surge in the availability of programmable machines for executing quantum algorithms 1,2. Present-day photonic quantum computers 3-7 have been limited either to non-deterministic operation, low photon numbers and rates, or fixed random gate sequences. Here we introduce a full-stack hardware-software system for executing many-photon quantum circuit operations using integrated nanophotonics: a programmable chip, operating at room temperature and interfaced with a fully automated control system. The system enables remote users to execute quantum algorithms that require up to eight modes of strongly squeezed vacuum initialized as two-mode squeezed states in single temporal modes, a fully general and programmable four-mode interferometer, and photon number-resolving readout on all outputs. Detection of multi-photon events with photon numbers and rates exceeding any previous programmable quantum optical demonstration is made possible by strong squeezing and high sampling rates. We verify the non-classicality of the device output, and use the platform to carry out proof-of-principle demonstrations of three quantum algorithms: Gaussian boson sampling, molecular vibronic spectra and graph similarity 8. These demonstrations validate the platform as a launchpad for scaling photonic technologies for quantum information processing.

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Crossref · 10.1038/s41586-021-03202-1

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  • Active demultiplexing increases the effective rate of PNR acquisition by a factor of 60 compared to previous systems.

    “active demultiplexing to increase the effective rate of PNR acquisition by a factor of 60, compared to previous systems40, by constructing a low-loss 1-to-16 binary switch tree and developing new photon-number extraction techniques”

    From Quantum computational advantage with a programmable photonic processor (2022) · cites this paper as [40]

    The Methods sections ‘Apparatus details’ and ‘Photon detection system’ describe the cited system’s 100-kHz pulse train and photon-number-resolving readout, supporting its use as a previous-system comparator. They do not report active demultiplexing or establish a 60-fold increase in the effective acquisition rate; that comparison cannot be verified from this source alone. (LLM, conf=0.82)

    Experimental — accuracy not yet measured
  • The relaxation time of the TES detectors is of the order of 10 to 20 μs, corresponding to 50-100 kHz, and depends on the expected photon number.

    “Relaxation time of our TES, back to baseline following illumination, is of the order of 10 to 20 μs corresponding to 50–100 kHz (ref. 40), and depends on the expected photon number.”

    From Quantum computational advantage with a programmable photonic processor (2022) · cites this paper as [40]

    Methods, ‘Photon detection system’ (page unavailable), says the TES detectors produce a voltage pulse every 10 μs, synchronized with the optical pulse train; ‘Apparatus details’ gives the train’s repetition rate as 100 kHz. The paper does not identify 10–20 μs as the detectors’ relaxation time or establish a 50–100 kHz relaxation-rate range. It says pulse shape depends on the number of incident photons, not that relaxation time depends on the expected photon number. (LLM, conf=0.84)

    Experimental — accuracy not yet measured
  • The latest experimental venture towards dynamically programmable GBS nanophotonic chip was carried out by Arrazola et al. 2021.

    “and the latest experimental venture towards dynamically programmable GBS nanophotonic chip was carried out by (Arrazola et al. , 2021).”

    From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Arrazola et al., 2021

  • The PNR detectors based on superconducting transition-edge sensors have 95% detection efficiency.

    “Spatial overlap is ensured by using single-mode fibre coupling at the entrance and exit of each loop delay, and samples are collected using an array of photon-number resolving (PNR) detectors based on superconducting transition-edge sensors (TES) with 95% detection efficiency39,40.”

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Cites

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Authors
Arrazola, J. M., Bergholm, V., Brádler, K., Bromley, T. R., Collins, M. J., Dhand, I., Fumagalli, A., Gerrits, T., Goussev, A., Helt, L. G., Hundal, J., Isacsson, T., Israel, R. B., Izaac, J., Jahangiri, S., Janik, R., Killoran, N., Kumar, S. P., Lavoie, J., Lita, A. E., Mahler, D. H., Menotti, M., Morrison, B., Nam, S. W., Neuhaus, L., Qi, H. Y., Quesada, N., Repingon, A., Sabapathy, K. K., Schuld, M., Su, D., Swinarton, J., Száva, A., Tan, K., Tan, P., Vaidya, V. D., Vernon, Z., Zabaneh, Z., & Zhang, Y.
Affiliations
  • Xanadu Quantum Technologies (Canada) (CA)
  • National Institute of Standards and Technology (US)
Corresponding author
  • Arrazola, J. M.
  • Vernon, Z.
Venue
Nature
Published
2021-03-03
Volume
591
Issue
7848
Pages
54-60
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
OpenAlex
W3133732393
Cited by
567
References
10
Citations by year
2026: 55 · 2025: 118 · 2024: 107 · 2023: 116 · 2022: 113 · 2021: 58
FWCI
34.81
Type
journal article
Language
en
Open access
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • PolyPublie (École Polytechnique de Montréal) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Photonic and Optical Devices
Keywords
photonic quantum computing, two-mode squeezed vacuum, photon-number-resolving detection, Gaussian boson sampling, graph similarity, nonclassicality

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