Toward Accurate Post-Born-Oppenheimer Molecular Simulations on Quantum Computers: An Adaptive Variational Eigensolver with Nuclear-Electronic Frozen Natural Orbitals
Artikel i vetenskaplig tidskrift, 2023

Nuclear quantum effects such as zero-point energy and hydrogen tunneling play a central role in many biological and chemical processes. The nuclear-electronic orbital (NEO) approach captures these effects by treating selected nuclei quantum mechanically on the same footing as electrons. On classical computers, the resources required for an exact solution of NEO-based models grow exponentially with system size. By contrast, quantum computers offer a means of solving this problem with polynomial scaling. However, due to the limitations of current quantum devices, NEO simulations are confined to the smallest systems described by minimal basis sets, whereas realistic simulations beyond the Born-Oppenheimer approximation require more sophisticated basis sets. For this purpose, we herein extend a hardware-efficient ADAPT-VQE method to the NEO framework in the frozen natural orbital (FNO) basis. We demonstrate on H2 and D2 molecules that the NEO-FNO-ADAPT-VQE method reduces the CNOT count by several orders of magnitude relative to the NEO unitary coupled cluster method with singles and doubles while maintaining the desired accuracy. This extreme reduction in the CNOT gate count is sufficient to permit practical computations employing the NEO method─an important step toward accurate simulations involving nonclassical nuclei and non-Born-Oppenheimer effects on near-term quantum devices. We further show that the method can capture isotope effects, and we demonstrate that inclusion of correlation energy systematically improves the prediction of difference in the zero-point energy (ΔZPE) between isotopes.

Författare

Anton Nykänen

Algorithmiq Ltd

Aaron Miller

Trinity College Dublin

Algorithmiq Ltd

Walter Talarico

Aalto-Yliopisto

Algorithmiq Ltd

Stefan Knecht

Algorithmiq Ltd

Eidgenössische Technische Hochschule Zürich (ETH)

Arseny Kovyrshin

AstraZeneca AB

Mårten Skogh

AstraZeneca AB

Chalmers, Kemi och kemiteknik, Kemi och biokemi

Lars Tornberg

AstraZeneca AB

Anders Broo

AstraZeneca AB

Stefano Mensa

STFC

Benjamin C.B. Symons

STFC

Emre Sahin

STFC

Jason Crain

IBM Research Europe

University of Oxford

Ivano Tavernelli

IBM Research

Fabijan Pavošević

Algorithmiq Ltd

Journal of Chemical Theory and Computation

1549-9618 (ISSN) 1549-9626 (eISSN)

Vol. 19 24 9269-9277

Ämneskategorier

Beräkningsmatematik

Teoretisk kemi

Datavetenskap (datalogi)

DOI

10.1021/acs.jctc.3c01091

PubMed

38081802

Mer information

Senast uppdaterat

2024-03-07