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

TL;DR

This paper demonstrates that a low-depth variational quantum algorithm can qualitatively reproduce key ground-state features of medium-sized Fermi-Hubbard model instances on a superconducting quantum processor, advancing quantum simulation of complex electronic systems.

Contribution

It introduces a scalable variational quantum algorithm capable of simulating larger Fermi-Hubbard instances beyond classical solvability, with novel error mitigation and optimization techniques.

Findings

Observed metal-insulator transition and Friedel oscillations in 1D.

Detected antiferromagnetic order in 1D and 2D.

Successfully scaled to 16 qubits on a superconducting processor.

Abstract

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 1x8 and 2x4 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. Our scalable approach is a first step to using near-term quantum computers to determine low-energy properties of strongly-correlated electronic systems that cannot be solved exactly by classical computers.