Physics-Informed Machine Learning Correction of Variational Quantum Eigensolver Energies for Molecular Ground States.
- DOI
- 10.1021/acsomega.6c04374
- Published
- 2026 Sep 22
- Container
- ACS omega
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1021/acsomega.6c04374,
title = {Physics-Informed Machine Learning Correction of Variational Quantum Eigensolver Energies for Molecular Ground States.},
author = {Sree KCS and Shankar V},
year = {2026},
journal = {ACS omega},
doi = {10.1021/acsomega.6c04374},
url = {https://doi.org/10.1021/acsomega.6c04374}
}RIS
TY - JOUR TI - Physics-Informed Machine Learning Correction of Variational Quantum Eigensolver Energies for Molecular Ground States. AU - Sree KCS AU - Shankar V PY - 2026 JO - ACS omega DO - 10.1021/acsomega.6c04374 UR - https://doi.org/10.1021/acsomega.6c04374 ER -
APA
KCS, S., & V, S. (2026). Physics-Informed Machine Learning Correction of Variational Quantum Eigensolver Energies for Molecular Ground States.. ACS omega. https://doi.org/10.1021/acsomega.6c04374