Author
Listed:
- C. Neill
(Google Quantum AI)
- T. McCourt
(Google Quantum AI)
- X. Mi
(Google Quantum AI)
- Z. Jiang
(Google Quantum AI)
- M. Y. Niu
(Google Quantum AI)
- W. Mruczkiewicz
(Google Quantum AI)
- I. Aleiner
(Google Quantum AI)
- F. Arute
(Google Quantum AI)
- K. Arya
(Google Quantum AI)
- J. Atalaya
(Google Quantum AI)
- R. Babbush
(Google Quantum AI)
- J. C. Bardin
(Google Quantum AI
University of Massachusetts)
- R. Barends
(Google Quantum AI)
- A. Bengtsson
(Google Quantum AI)
- A. Bourassa
(Google Quantum AI
University of Chicago)
- M. Broughton
(Google Quantum AI)
- B. B. Buckley
(Google Quantum AI)
- D. A. Buell
(Google Quantum AI)
- B. Burkett
(Google Quantum AI)
- N. Bushnell
(Google Quantum AI)
- J. Campero
(Google Quantum AI)
- Z. Chen
(Google Quantum AI)
- B. Chiaro
(Google Quantum AI)
- R. Collins
(Google Quantum AI)
- W. Courtney
(Google Quantum AI)
- S. Demura
(Google Quantum AI)
- A. R. Derk
(Google Quantum AI)
- A. Dunsworth
(Google Quantum AI)
- D. Eppens
(Google Quantum AI)
- C. Erickson
(Google Quantum AI)
- E. Farhi
(Google Quantum AI)
- A. G. Fowler
(Google Quantum AI)
- B. Foxen
(Google Quantum AI)
- C. Gidney
(Google Quantum AI)
- M. Giustina
(Google Quantum AI)
- J. A. Gross
(Google Quantum AI)
- M. P. Harrigan
(Google Quantum AI)
- S. D. Harrington
(Google Quantum AI)
- J. Hilton
(Google Quantum AI)
- A. Ho
(Google Quantum AI)
- S. Hong
(Google Quantum AI)
- T. Huang
(Google Quantum AI)
- W. J. Huggins
(Google Quantum AI)
- S. V. Isakov
(Google Quantum AI)
- M. Jacob-Mitos
(Google Quantum AI)
- E. Jeffrey
(Google Quantum AI)
- C. Jones
(Google Quantum AI)
- D. Kafri
(Google Quantum AI)
- K. Kechedzhi
(Google Quantum AI)
- J. Kelly
(Google Quantum AI)
- S. Kim
(Google Quantum AI)
- P. V. Klimov
(Google Quantum AI)
- A. N. Korotkov
(Google Quantum AI
University of California, Riverside)
- F. Kostritsa
(Google Quantum AI)
- D. Landhuis
(Google Quantum AI)
- P. Laptev
(Google Quantum AI)
- E. Lucero
(Google Quantum AI)
- O. Martin
(Google Quantum AI)
- J. R. McClean
(Google Quantum AI)
- M. McEwen
(Google Quantum AI
University of California, Santa Barbara)
- A. Megrant
(Google Quantum AI)
- K. C. Miao
(Google Quantum AI)
- M. Mohseni
(Google Quantum AI)
- J. Mutus
(Google Quantum AI)
- O. Naaman
(Google Quantum AI)
- M. Neeley
(Google Quantum AI)
- M. Newman
(Google Quantum AI)
- T. E. O’Brien
(Google Quantum AI)
- A. Opremcak
(Google Quantum AI)
- E. Ostby
(Google Quantum AI)
- B. Pató
(Google Quantum AI)
- A. Petukhov
(Google Quantum AI)
- C. Quintana
(Google Quantum AI)
- N. Redd
(Google Quantum AI)
- N. C. Rubin
(Google Quantum AI)
- D. Sank
(Google Quantum AI)
- K. J. Satzinger
(Google Quantum AI)
- V. Shvarts
(Google Quantum AI)
- D. Strain
(Google Quantum AI)
- M. Szalay
(Google Quantum AI)
- M. D. Trevithick
(Google Quantum AI)
- B. Villalonga
(Google Quantum AI)
- T. C. White
(Google Quantum AI)
- Z. Yao
(Google Quantum AI)
- P. Yeh
(Google Quantum AI)
- A. Zalcman
(Google Quantum AI)
- H. Neven
(Google Quantum AI)
- S. Boixo
(Google Quantum AI)
- L. B. Ioffe
(Google Quantum AI)
- P. Roushan
(Google Quantum AI)
- Y. Chen
(Google Quantum AI)
- V. Smelyanskiy
(Google Quantum AI)
Abstract
A promising approach to study condensed-matter systems is to simulate them on an engineered quantum platform1–4. However, the accuracy needed to outperform classical methods has not been achieved so far. Here, using 18 superconducting qubits, we provide an experimental blueprint for an accurate condensed-matter simulator and demonstrate how to investigate fundamental electronic properties. We benchmark the underlying method by reconstructing the single-particle band structure of a one-dimensional wire. We demonstrate nearly complete mitigation of decoherence and readout errors, and measure the energy eigenvalues of this wire with an error of approximately 0.01 rad, whereas typical energy scales are of the order of 1 rad. Insight into the fidelity of this algorithm is gained by highlighting the robust properties of a Fourier transform, including the ability to resolve eigenenergies with a statistical uncertainty of 10−4 rad. We also synthesize magnetic flux and disordered local potentials, which are two key tenets of a condensed-matter system. When sweeping the magnetic flux we observe avoided level crossings in the spectrum, providing a detailed fingerprint of the spatial distribution of local disorder. By combining these methods we reconstruct electronic properties of the eigenstates, observing persistent currents and a strong suppression of conductance with added disorder. Our work describes an accurate method for quantum simulation5,6 and paves the way to study new quantum materials with superconducting qubits.
Suggested Citation
C. Neill & T. McCourt & X. Mi & Z. Jiang & M. Y. Niu & W. Mruczkiewicz & I. Aleiner & F. Arute & K. Arya & J. Atalaya & R. Babbush & J. C. Bardin & R. Barends & A. Bengtsson & A. Bourassa & M. Brought, 2021.
"Accurately computing the electronic properties of a quantum ring,"
Nature, Nature, vol. 594(7864), pages 508-512, June.
Handle:
RePEc:nat:nature:v:594:y:2021:i:7864:d:10.1038_s41586-021-03576-2
DOI: 10.1038/s41586-021-03576-2
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