Observation of separated dynamics of charge and spin in the Fermi-Hubbard model
Cite this dataset
Jiang, Zhang; Mruczkiewicz, Wojciech; Smelyanskiy, Vadim (2020). Observation of separated dynamics of charge and spin in the Fermi-Hubbard model [Dataset]. Dryad. https://doi.org/10.5061/dryad.crjdfn32v
Strongly correlated quantum systems give rise to many exotic physical phenomena, including high-temperature superconductivity. Simulating these systems on quantum computers may avoid the prohibitively high computational cost incurred in classical approaches. However, systematic errors and decoherence effects presented in current quantum devices make it difficult to achieve this. Here, we simulate the dynamics of the one-dimensional Fermi-Hubbard model using 16 qubits on a digital superconducting quantum processor. We observe separations in the spreading velocities of charge and spin densities in the highly excited regime, a regime that is beyond the conventional quasiparticle picture. To minimize systematic errors, we introduce an accurate gate calibration procedure that is fast enough to capture temporal drifts of the gate parameters. We also employ a sequence of error-mitigation techniques to reduce decoherence effects and residual systematic errors. These procedures allow us to simulate the time evolution of the model faithfully despite having over 600 two-qubit gates in our circuits. Our experiment charts a path to practical quantum simulation of strongly correlated phenomena using available quantum devices.
These contain raw data collected from Google's Sycamore superconducting quantum processor as well as processed data with associated metadata. The code that used to process the data can be found at https://github.com/quantumlib/ReCirq.
A README file is included. Data are stored in Cirq compatible JSON format.