Symmetry-Blocked Matrix Product States as a Neural-Network Quantum-State Ansatz for Quantum Chemistry.

Fu L, Kan B, Guo C, Shang H

DOI: 10.1021/acs.jctc.6c01040
Published: 2026 Aug 25
Container: Journal of chemical theory and computation
Publisher: Not recorded
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Cite this work

BibTeX

@article{allodium:10.1021/acs.jctc.6c01040,
  title = {Symmetry-Blocked Matrix Product States as a Neural-Network Quantum-State Ansatz for Quantum Chemistry.},
  author = {Fu L and Kan B and Guo C and Shang H},
  year = {2026},
  journal = {Journal of chemical theory and computation},
  doi = {10.1021/acs.jctc.6c01040},
  url = {https://doi.org/10.1021/acs.jctc.6c01040}
}

RIS

TY  - JOUR
TI  - Symmetry-Blocked Matrix Product States as a Neural-Network Quantum-State Ansatz for Quantum Chemistry.
AU  - Fu L
AU  - Kan B
AU  - Guo C
AU  - Shang H
PY  - 2026
JO  - Journal of chemical theory and computation
DO  - 10.1021/acs.jctc.6c01040
UR  - https://doi.org/10.1021/acs.jctc.6c01040
ER  -

APA

L, F., B, K., C, G., & H, S. (2026). Symmetry-Blocked Matrix Product States as a Neural-Network Quantum-State Ansatz for Quantum Chemistry.. Journal of chemical theory and computation. https://doi.org/10.1021/acs.jctc.6c01040

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pubmed · retrieved 2026-09-25T19:59:34.373Z