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Quantum computational advantage via high-dimensional Gaussian boson sampling

Deshpande, Abhinav and Mehta, Arthur and Vincent, Trevor and Quesada, Nicolás and Hinsche, Marcel and Ioannou, Marios and Madsen, Lars and Lavoie, Jonathan and Qi, Haoyu and Eisert, Jens and Hangleiter, Dominik and Fefferman, Bill and Dhand, Ish (2022) Quantum computational advantage via high-dimensional Gaussian boson sampling. Science Advances, 8 (1). Art. No. abi7894. ISSN 2375-2548. PMCID PMC8730598. doi:10.1126/sciadv.abi7894.

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Photonics is a promising platform for demonstrating a quantum computational advantage (QCA) by outperforming the most powerful classical supercomputers on a well-defined computational task. Despite this promise, existing proposals and demonstrations face challenges. Experimentally, current implementations of Gaussian boson sampling (GBS) lack programmability or have prohibitive loss rates. Theoretically, there is a comparative lack of rigorous evidence for the classical hardness of GBS. In this work, we make progress in improving both the theoretical evidence and experimental prospects. We provide evidence for the hardness of GBS, comparable to the strongest theoretical proposals for QCA. We also propose a QCA architecture we call high-dimensional GBS, which is programmable and can be implemented with low loss using few optical components. We show that particular algorithms for simulating GBS are outperformed by high-dimensional GBS experiments at modest system sizes. This work thus opens the path to demonstrating QCA with programmable photonic processors.

Item Type:Article
Related URLs:
URLURL TypeDescription CentralArticle
Deshpande, Abhinav0000-0002-6114-1830
Mehta, Arthur0000-0002-6384-5196
Quesada, Nicolás0000-0002-0175-1688
Hinsche, Marcel0000-0003-4174-5706
Qi, Haoyu0000-0002-0636-7925
Eisert, Jens0000-0003-3033-1292
Hangleiter, Dominik0000-0002-4766-7967
Fefferman, Bill0000-0002-9627-0210
Dhand, Ish0000-0002-7041-2575
Additional Information:© 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). Submitted 31 March 2021; Accepted 12 November 2021; Published 5 January 2022. We thank J. M. Arrazola, L. G. Helt, M. Collins, F. Laudenbach, I. Tzitrin, and Z. Vernon for helpful discussions. We also thank Bouland and colleagues (13) for sharing an early version of their manuscript. M.H., M.I., and D.H. thank K. Zyczkowski for enlightening discussions regarding the distribution of COE submatrices. A.M. is funded by Mitacs Accelerate Program. M.H., M.I., J.E., and D.H. are funded by the DFG (EI 519/21-1, EI 519/9-1, EI 519/14-1, and CRC 183), the MATH+ Cluster of Excellence, the BMBF (HYBRID), the Einstein Research Foundation (Einstein Research Unit on near-term quantum devices), BMBF (QPIC), BMBF (PhoQuant), and the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 817482 (PASQuanS). The authors thank SOSCIP and SciNet for computational resources. Computations have been performed on the Niagara and the Mist supercomputers at the SciNet-SOSCIP HPC Consortium. SciNet is funded by The Canada Foundation for Innovation, the government of Ontario; Ontario Research Fund–Research Excellence; and the University of Toronto. SOSCIP is funded by the Federal Economic Development Agency of Southern Ontario, the province of Ontario, IBM Canada Ltd., Ontario Centres of Excellence, Mitacs, and Ontario academic member institutions. Author contributions: Theory work was completed by A.D., A.M., M.H., M.I., H.Q., J.E., D.H., and B. F. T.V., N.Q., L.M., and J.L. completed the experimental design and benchmarking work. I.D. managed the project and made contributions to both the theory and benchmarking work. All authors discussed the results and contributed to writing the manuscript. Competing interests: B.F. and A.D. acted as paid consultants for Xanadu Quantum Technologies while parts of this work were being performed. The authors declare that they have no other competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.
Group:Institute for Quantum Information and Matter
Funding AgencyGrant Number
Deutsche Forschungsgemeinschaft (DFG)EI 519/21-1
Deutsche Forschungsgemeinschaft (DFG)EI 519/9-1
Deutsche Forschungsgemeinschaft (DFG)EI 519/14-1
Deutsche Forschungsgemeinschaft (DFG)CRC 183
MATH+ Cluster of ExcellenceUNSPECIFIED
Bundesministerium für Bildung und Forschung (BMBF)UNSPECIFIED
Einstein Research FoundationUNSPECIFIED
European Research Council (ERC)817482
Canada Foundation for InnovationUNSPECIFIED
Ontario Research Fund-Research ExcellenceUNSPECIFIED
University of TorontoUNSPECIFIED
Federal Economic Development Agency of Southern OntarioUNSPECIFIED
Province of OntarioUNSPECIFIED
Issue or Number:1
PubMed Central ID:PMC8730598
Record Number:CaltechAUTHORS:20220111-884895300
Persistent URL:
Official Citation:Quantum computational advantage via high-dimensional Gaussian boson sampling. Abhinav Deshpande, Arthur Mehta, Trevor Vincent, Nicolás Quesada, Marcel Hinsche, Marios Ioannou, Lars Madsen, Jonathan Lavoie, Haoyu Qi, Jens Eisert, Dominik Hangleiter, Bill Fefferman, Ish Dhand Sci. Adv., 8 (1), eabi7894; DOI: 10.1126/sciadv.abi7894
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112818
Deposited By: Tony Diaz
Deposited On:11 Jan 2022 17:17
Last Modified:21 Jan 2022 16:51

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