Work

Research data supporting "Exponential Scaling Barriers for Variational Quantum Eigensolvers"

Year: 2026

Type: article

Abstract: This data contains additional information on the molecules in the MolVQE-21 benchmark dataset. Further it contains the energy, spin and gradient information for the ADAPT-VQE runs in each ADAPT iteration, which are necessary to generate the figures in "Exponential Scaling Barriers for Variational Quantum Eigensolvers". The file data_format.txt contains additional information about the data structure.This work was supported by the German Federal Ministry of Research, Technology and Space through the project H2Giga-DegradEL3 (grant no. 03HY110D).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19051535

Authors: Hagelueken, Manuel, Kreplin, David A., Wieland, Florian, Huber, Marco F., Roth, Marco

Institutions: Fraunhofer Institute for Manufacturing Engineering and Automation, Heilbronn University, Esslingen University of Applied Sciences

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7140212209

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