Sanaa Abaach, Zakaria Mzaouali, and Morad El Baz
This repository is dedicated to the research on Long distance entanglement and high-dimensional quantum teleportation in the Fermi–Hubbard model. The primary focus is on the peculiarities of the Fermi-Hubbard model's ground state and its capability to sustain maximum long-distance entanglement, thus functioning as a quantum resource for high fidelity quantum teleportation over long distances.
- Ground State Entanglement: Analysis of the ground state of the Fermi-Hubbard model for optimal long-distance entanglement.
- Scalability and Stability: Determination of the physical properties required for scalable entanglement and its stability analysis under Coulomb interaction and hopping amplitude variations.
- Teleportation Fidelity: Investigation of how the selection of measurement bases influences the fidelity of quantum teleportation.
- Hubbard Projective Measurements: Argumentation for the selection of an adequate measurement basis that aligns with the characteristics of the quantum channel to achieve perfect information transfer.
quantum teleporation, quantum entanglement, Fermi-Hubbard model
- 'src/': Includes all source code used for simulations and analysis.
- 'data/': Includes all the data generated via source code.
- 'figures/': Compiled results and graphs presented in the paper.
S.A. acknowledges gratefully the National Center for Scientific and Technical Research (CNRST) for financial support (Grant No. 1UM5R2018). Z.M. acknowledges support from the National Science Center (NCN), Poland, under Project No. 2020/38/E/ST3/00269. This research is supported through computational resources of HPC- MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco.
If you find this research useful, please cite it under:
@article{abaach2023,
title={Long distance entanglement and high-dimensional quantum teleportation in the Fermi--Hubbard model},
author={Abaach, Sanaa and Mzaouali, Zakaria and El Baz, Morad},
journal={Scientific Reports},
volume={13},
number={1},
pages={964},
year={2023},
publisher={Nature Publishing Group UK London}
}