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Published February 23, 2023 | Published + Supplemental Material
Journal Article Open

Suppressing quantum errors by scaling a surface code logical qubit

Acharya, Rajeev
Aleiner, Igor ORCID icon
Allen, Richard M.
Andersen, Trond I.
Ansmann, Markus ORCID icon
Arute, Frank
Arya, Kunal ORCID icon
Asfaw, Abraham
Atalaya, Juan ORCID icon
Babbush, Ryan ORCID icon
Bacon, Dave
Bardin, Joseph C. ORCID icon
Basso, Joao ORCID icon
Bengtsson, Andreas ORCID icon
Boixo, Sergio ORCID icon
Bortoli, Gina
Bourassa, Alexandre ORCID icon
Bovaird, Jenna
Brill, Leon
Broughton, Michael
Buckley, Bob B. ORCID icon
Buell, David A.
Burger, Tim
Burkett, Brian ORCID icon
Bushnell, Nicholas ORCID icon
Chen, Yu
Chen, Zijun
Chiaro, Benjamin ORCID icon
Cogan, Josh
Collins, Roberto ORCID icon
Conner, Paul
Courtney, William
Crook, Alexander L.
Curtin, Ben
Debroy, Dripto M.
Del Toro Barba, Alexander ORCID icon
Demura, Sean ORCID icon
Dunsworth, Andrew
Eppens, Daniel ORCID icon
Erickson, Catherine
Faoro, Lara
Farhi, Edward
Fatemi, Reza ORCID icon
Flores Burgos, Leslie
Forati, Ebrahim
Fowler, Austin G. ORCID icon
Foxen, Brooks ORCID icon
Giang, William
Gidney, Craig
Gilboa, Dar
Giustina, Marissa
Grajales Dau, Alejandro
Gross, Jonathan A. ORCID icon
Habegger, Steve ORCID icon
Hamilton, Michael C.
Harrigan, Matthew P. ORCID icon
Harrington, Sean D. ORCID icon
Higgott, Oscar
Hilton, Jeremy
Hoffmann, Markus ORCID icon
Hong, Sabrina
Huang, Trent
Huff, Ashley
Huggins, William J. ORCID icon
Ioffe, Lev B.
Isakov, Sergei V.
Iveland, Justin
Jeffrey, Evan
Jiang, Zhang ORCID icon
Jones, Cody
Juhas, Pavol ORCID icon
Kafri, Dvir ORCID icon
Kechedzhi, Kostyantyn ORCID icon
Kelly, Julian ORCID icon
Khattar, Tanuj
Khezri, Mostafa ORCID icon
Kieferová, Mária ORCID icon
Kim, Seon
Kitaev, Alexei1 ORCID icon
Klimov, Paul V. ORCID icon
Klots, Andrey R.
Korotkov, Alexander N.
Kostritsa, Fedor
Kreikebaum, John Mark ORCID icon
Landhuis, David ORCID icon
Laptev, Pavel ORCID icon
Lau, Kim-Ming
Laws, Lily
Lee, Joonho
Lee, Kenny
Lester, Brian J. ORCID icon
Lill, Alexander
Liu, Wayne
Locharla, Aditya ORCID icon
Lucero, Erik ORCID icon
Malone, Fionn D. ORCID icon
Marshall, Jeffrey ORCID icon
Martin, Orion ORCID icon
McClean, Jarrod R. ORCID icon
McCourt, Trevor ORCID icon
McEwen, Matt ORCID icon
Megrant, Anthony ORCID icon
Meurer Costa, Bernardo
Mi, Xiao ORCID icon
Miao, Kevin C. ORCID icon
Mohseni, Masoud ORCID icon
Montazeri, Shirin ORCID icon
Morvan, Alexis ORCID icon
Mount, Emily
Mruczkiewicz, Wojciech ORCID icon
Naaman, Ofer ORCID icon
Neeley, Matthew ORCID icon
Neill, Charles ORCID icon
Nersisyan, Ani
Neven, Hartmut ORCID icon
Newman, Michael
Ng, Jiun How ORCID icon
Nguyen, Anthony ORCID icon
Nguyen, Murray
Niu, Murphy Yuezhen
O'Brien, Thomas E. ORCID icon
Opremcak, Alex
Platt, John ORCID icon
Petukhov, Andre
Potter, Rebecca
Pryadko, Leonid P. ORCID icon
Quintana, Chris ORCID icon
Roushan, Pedram ORCID icon
Rubin, Nicholas C. ORCID icon
Saei, Negar
Sank, Daniel ORCID icon
Sankaragomathi, Kannan ORCID icon
Satzinger, Kevin J. ORCID icon
Schurkus, Henry F. ORCID icon
Schuster, Christopher
Shearn, Michael J.
Shorter, Aaron
Shvarts, Vladimir
Skruzny, Jindra
Smelyanskiy, Vadim ORCID icon
Smith, W. Clarke
Sterling, George ORCID icon
Strain, Doug
Szalay, Marco ORCID icon
Torres, Alfredo
Vidal, Guifre
Villalonga, Benjamin ORCID icon
Vollgraff Heidweiller, Catherine ORCID icon
White, Theodore
Xing, Cheng
Yao, Z. Jamie
Yeh, Ping ORCID icon
Yoo, Juhwan
Young, Grayson
Zalcman, Adam ORCID icon
Zhang, Yaxing
Zhu, Ningfeng ORCID icon
Google Quantum AI
  • 1. ROR icon California Institute of Technology

Abstract

Practical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction1,2 offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size. Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 ± 0.016)% compared to (3.028 ± 0.023)%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10⁻⁶ logical error per cycle floor set by a single high-energy event (1.6 × 10⁻⁷ excluding this event). We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.

Additional Information

© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. We are grateful to S. Brin, S. Pichai, R. Porat, J. Dean, E. Collins and J. Yagnik for their executive sponsorship of the Google Quantum AI team, and for their continued engagement and support. A portion of this work was performed in the University of California, Santa Barbara Nanofabrication Facility, an open access laboratory. J.M. acknowledges support from the National Aeronautics and Space Administration (NASA) Ames Research Center (NASA-Google SAA 403512), NASA Advanced Supercomputing Division for access to NASA high-performance computing systems, and NASA Academic Mission Services (NNA16BD14C). D.B. is a CIFAR Associate Fellow in the Quantum Information Science Program. Data availability: The data that support the findings of this study are available at https://doi.org/10.5281/zenodo.6804040. Contributions: The Google Quantum AI team conceived and designed the experiment. The theory and experimental teams at Google Quantum AI developed the data analysis, modelling and metrological tools that enabled the experiment, built the system, performed the calibrations and collected the data. The modelling was carried out jointly with collaborators outside Google Quantum AI. All authors wrote and revised the manuscript and the Supplementary Information. The authors declare no competing interests.

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Supplemental Material - 41586_2022_5434_MOESM1_ESM.pdf

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Additional details

Created:
August 22, 2023
Modified:
February 9, 2024