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Quantum supremacy using a programmable superconducting processor

Arute, Frank and Brandão, Fernando G. S. L. (2019) Quantum supremacy using a programmable superconducting processor. Nature, 574 (7779). pp. 505-510. ISSN 0028-0836.

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The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 2⁵³ (about 10¹⁶). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy for this specific computational task, heralding a much-anticipated computing paradigm.

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Brandão, Fernando G. S. L.0000-0003-3866-9378
Additional Information:© 2019 Springer Nature Limited. Received 22 July 2019; Accepted 20 September 2019; Published 23 October 2019; Issue Date 24 October 2019. Data availability: The datasets generated and analysed for this study are available at our public Dryad repository ( We are grateful to E. Schmidt, S. Brin, S. Pichai, J. Dean, J. Yagnik and J. Giannandrea for their executive sponsorship of the Google AI Quantum team, and for their continued engagement and support. We thank P. Norvig, J. Yagnik, U. Hölzle and S. Pichai for advice on the manuscript. We acknowledge K. Kissel, J. Raso, D. L. Yonge-Mallo, O. Martin and N. Sridhar for their help with simulations. We thank G. Bortoli and L. Laws for keeping our team organized. This research used resources from the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility (supported by contract DE-AC05-00OR22725). A portion of this work was performed in the UCSB Nanofabrication Facility, an open access laboratory. R.B., S.M., and E.G.R. appreciate support from the NASA Ames Research Center and from the Air Force Research (AFRL) Information Directorate (grant F4HBKC4162G001). T.S.H. is supported by the DOE Early Career Research Program. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of AFRL or the US government. Author Contributions: The Google AI Quantum team conceived the experiment. The applications and algorithms team provided the theoretical foundation and the specifics of the algorithm. The hardware team carried out the experiment and collected the data. The data analysis was done jointly with outside collaborators. All authors wrote and revised the manuscript and the Supplementary Information. The authors declare no competing interests.
Group:Institute for Quantum Information and Matter
Funding AgencyGrant Number
Department of Energy (DOE)DE-AC05-00OR22725
Air Force Research Laboratory (AFRL)F4HBKC4162G001
Issue or Number:7779
Record Number:CaltechAUTHORS:20191028-155653039
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99516
Deposited By: Tony Diaz
Deposited On:28 Oct 2019 23:08
Last Modified:06 May 2021 23:00

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