Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor
Gavin S. Hartnett, Khadijeh Sona Najafi, Aleksei Khindanov, Haoran Liao, Michael Schutzman, Michael R. Hush, Michael J. Biercuk, Yuval Baum

TL;DR
This paper demonstrates large-scale, high-resolution digital quantum simulation of the 1D Fermi-Hubbard model on a superconducting quantum processor, surpassing classical simulation capabilities in accuracy and speed.
Contribution
It introduces an efficient encoding and error suppression techniques enabling simulation of large fermionic systems with up to 120 qubits, achieving results beyond classical methods.
Findings
Direct observation of spin-charge separation at scale
Quantum outputs match classical TDVP simulations within 1% RMSE
Quantum processor runs up to 3000 times faster than classical TDVP for long evolutions
Abstract
We report experimental digital quantum simulation of the one-dimensional Fermi-Hubbard model on a superconducting quantum processor at a scale beyond the reach of exact statevector simulation and challenging for state-of-the-art tensor-network methods. We encode this problem using up to 120 qubits through an efficient mapping that reduces circuit complexity, and we improve accuracy through error suppression to simulate dynamical evolution using up to 90 Trotter steps. From a vacancy defect introduced in the middle of an -site (62-qubit) N\'{e}el initial state, we directly observe spin-charge separation to in natural units using up to 90 Trotter steps, and quantitatively extract velocity ratios which match classical simulations across a range of model parameters. We then extend experiments to (120 qubits) and long evolution times to using 30 Trotter…
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