Range‐separated density functional theory using multiresolution analysis and quantum computing

  • Quantum computers are expected to outperform classical computers for specific problems in quantum chemistry. Such calculations remain expensive, but costs can be lowered through the partition of the molecular system. In the present study, partition was achieved with range-separated density functional theory (RS-DFT). The use of RS-DFT reduces both the basis set size and the active space size dependence of the ground state energy in comparison with the use of wave function theory (WFT) alone. The utilization of pair natural orbitals (PNOs) in place of canonical molecular orbitals (MOs) results in more compact qubit Hamiltonians. To test this strategy, a basis-set independent framework, known as multiresolution analysis (MRA), was employed to generate PNOs. Tests were conducted with the variational quantum eigensolver for a number of molecules. The results show that the proposed approach reduces the number of qubits needed to reach a target energy accuracy.

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Metadaten
Author:Nicolas Poirier, Jakob S. KottmannORCiDGND, Alán Aspuru‐Guzik, Luc Mongeau, Alireza Najafi‐Yazdi
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/113131
ISSN:0192-8651OPAC
ISSN:1096-987XOPAC
Parent Title (English):Journal of Computational Chemistry
Publisher:Wiley
Type:Article
Language:English
Year of first Publication:2024
Publishing Institution:Universität Augsburg
Release Date:2024/05/22
DOI:https://doi.org/10.1002/jcc.27384
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Professur für Quantenalgorithmik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Latest Publications (not yet published in print):Aktuelle Publikationen (noch nicht gedruckt erschienen)
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)