Observing ground-state properties of the Fermi-Hubbard model using a scalable algorithm on a quantum computer.

Autor: Stanisic S; Phasecraft Ltd., Bristol, UK., Bosse JL; Phasecraft Ltd., Bristol, UK.; School of Mathematics, University of Bristol, Bristol, UK., Gambetta FM; Phasecraft Ltd., Bristol, UK., Santos RA; Phasecraft Ltd., London, UK., Mruczkiewicz W; Google Quantum AI, Mountain View, CA, USA., O'Brien TE; Google Quantum AI, Mountain View, CA, USA., Ostby E; Google Quantum AI, Mountain View, CA, USA., Montanaro A; Phasecraft Ltd., Bristol, UK. ashley@phasecraft.io.; School of Mathematics, University of Bristol, Bristol, UK. ashley@phasecraft.io.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2022 Oct 11; Vol. 13 (1), pp. 5743. Date of Electronic Publication: 2022 Oct 11.
DOI: 10.1038/s41467-022-33335-4
Abstrakt: The famous, yet unsolved, Fermi-Hubbard model for strongly-correlated electronic systems is a prominent target for quantum computers. However, accurately representing the Fermi-Hubbard ground state for large instances may be beyond the reach of near-term quantum hardware. Here we show experimentally that an efficient, low-depth variational quantum algorithm with few parameters can reproduce important qualitative features of medium-size instances of the Fermi-Hubbard model. We address 1 × 8 and 2 × 4 instances on 16 qubits on a superconducting quantum processor, substantially larger than previous work based on less scalable compression techniques, and going beyond the family of 1D Fermi-Hubbard instances, which are solvable classically. Consistent with predictions for the ground state, we observe the onset of the metal-insulator transition and Friedel oscillations in 1D, and antiferromagnetic order in both 1D and 2D. We use a variety of error-mitigation techniques, including symmetries of the Fermi-Hubbard model and a recently developed technique tailored to simulating fermionic systems. We also introduce a new variational optimisation algorithm based on iterative Bayesian updates of a local surrogate model.
(© 2022. The Author(s).)
Databáze: MEDLINE
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