Variational solutions to fermion-to-qubit mappings in two spatial dimensions

Jannes Nys; Giuseppe Carleo

doi:10.22331/q-2022-10-13-833

Variational solutions to fermion-to-qubit mappings in two spatial dimensions

École Polytechnique Fédérale de Lausanne (EPFL), Institute of Physics, CH-1015 Lausanne, Switzerland
Center for Quantum Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

Published:	2022-10-13, volume 6, page 833
Eprint:	arXiv:2205.00733v2
Doi:	https://doi.org/10.22331/q-2022-10-13-833
Citation:	Quantum 6, 833 (2022).

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Abstract

Through the introduction of auxiliary fermions, or an enlarged spin space, one can map local fermion Hamiltonians onto local spin Hamiltonians, at the expense of introducing a set of additional constraints. We present a variational Monte-Carlo framework to study fermionic systems through higher-dimensional ($\gt$1D) Jordan-Wigner transformations. We provide exact solutions to the parity and Gauss-law constraints that are encountered in bosonization procedures. We study the $t$-$V$ model in 2D and demonstrate how both the ground state and the low-energy excitation spectra can be retrieved in combination with neural network quantum state ansatze.

Popular summary

One can map local fermion Hamiltonians onto local spin Hamiltonians, at the expense of introducing a set of additional (gauge) constraints. We present a variational Monte-Carlo framework to study fermionic systems through higher-dimensional (>1D) Jordan-Wigner transformations.

► BibTeX data

@article{Nys2022variational,
  doi = {10.22331/q-2022-10-13-833},
  url = {https://doi.org/10.22331/q-2022-10-13-833},
  title = {Variational solutions to fermion-to-qubit mappings in two spatial dimensions},
  author = {Nys, Jannes and Carleo, Giuseppe},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {6},
  pages = {833},
  month = oct,
  year = {2022}
}

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Cited by

[1] Filippo Vicentini, Damian Hofmann, Attila Szabó, Dian Wu, Christopher Roth, Clemens Giuliani, Gabriel Pescia, Jannes Nys, Vladimir Vargas-Calderon, Nikita Astrakhantsev, and Giuseppe Carleo, "NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems", arXiv:2112.10526, (2021).

[2] Adam Wyrzykowski, "Local spin description of fermions on a lattice", arXiv:2206.10393, (2022).

The above citations are from SAO/NASA ADS (last updated successfully 2023-03-20 06:58:44). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref's cited-by service no data on citing works was found (last attempt 2023-03-20 06:58:43).

This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.