Google Quantum AI; Rajeev Acharya; I. L. Aleǐner; R. M. Allen; Trond I. Andersen; M. Ansmann; Frank Arute; Kunal Arya; Abraham Asfaw; Juan Atalaya; Ryan Babbush; Dave Bacon; Joseph C. Bardin; Joao Basso; Andreas Bengtsson; Sergio Boixo; Gina Bortoli; Alexandre Bourassa; Jenna Bovaird; Leon Brill; Michael Broughton; Bob B. Buckley; David A. Buell; Tim Burger; Brian Burkett; Nicholas Bushnell; Yu Chen; Zijun Chen; B. Chiaro; Josh Cogan; Roberto Collins; Paul Conner; William Courtney; Alexander L. Crook; Ben Curtin; Dripto M. Debroy; Alexander Del Toro Barba; Sean Demura; A. Dunsworth; Daniel Eppens; Catherine Erickson; Lara Faoro; Edward Farhi; Reza Fatemi; Leslie Flores Burgos; Ebrahim Forati; Austin G. Fowler; Brooks Foxen; W. Giang; Craig Gidney; Dar Gilboa; Marissa Giustina; Alejandro Grajales Dau; Jonathan A. Gross; Steve Habegger; Michael C. Hamilton; Matthew P. Harrigan; Sean D. Harrington; Oscar Higgott; Jeremy Hilton; M. R. Hoffmann; Sabrina Hong; Trent Huang; Ashley Huff; William J. Huggins; L. B. Ioffe; Sergei V. Isakov; Justin Iveland; E. Jeffrey; Jiang Zhang; Cody Jones; Pavol Juhás; Dvir Kafri; Kostyantyn Kechedzhi; J. Kelly; Tanuj Khattar; Mostafa Khezri; Mária Kieferová; Seon Kim; Alexei Kitaev; Paul V. Klimov; Andrey R. Klots; Alexander N. Korotkov; Fedor Kostritsa; John Mark Kreikebaum; David Landhuis; Pavel Laptev; Kim-Ming Lau; Lily Laws; Joonho Lee; Kenny Lee; Brian Lester; Alexander T. Lill; Wayne Liu; Aditya Locharla; Erik Lucero; Fionn D. Malone; Jeffrey Marshall; Orion Martin; Jarrod R. McClean · 2023 · Nature
Paper
Abstract Practical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction 1,2 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.
Analysis
This paper demonstrates that increasing the number of physical qubits in a surface code can improve logical qubit performance, overcoming additional error sources introduced by scaling.
Discovery
V. G. Matsos; C. H. Valahu; M. J. Millican; T. Navickas; X. C. Kolesnikow; M. J. Biercuk; T. R. Tan
Tom Peham; Ludwig Schmid; Lucas Berent; Markus Müller; Robert Wille
Bence Hetényi; James R. Wootton
Hayato Goto
Amara Katabarwa; Katerina Gratsea; Athena Caesura; Peter D. Johnson
Paul V. Klimov; Andreas Bengtsson; Chris Quintana; Alexandre Bourassa; Sabrina Hong; A. Dunsworth; Kevin J. Satzinger; William P. Livingston; Volodymyr Sivak; Murphy Yuezhen Niu; Trond I. Andersen; Yaxing Zhang; Desmond Chik; Zijun Chen; Charles Neill; Catherine Erickson; Alejandro Grajales Dau; A. Megrant; P. Roushan; Alexander N. Korotkov; J. Kelly; Vadim Smelyanskiy; Yu Chen; Hartmut Neven
Source record