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Accelerating Variational Quantum Eigensolver Convergence using Parameter Transfer

SND-ID: 2023-143. Version: 1. DOI: https://doi.org/10.5878/fvza-z272

Citation

Creator/Principal investigator(s)

Mårten Skogh - Chalmers University of Technology orcid

Martin Rahm - Chalmers University of Technology, Department of Chemistry and Chemical Engineering orcid

Research principal

Chalmers University of Technology rorId

Description

Results of quantum computer simulations, using the Variational Quantum Eigensolver (VQE). The dataset contains data calculated using various parameters and settings. The goal of the data is to see how these parameters and settings affect the potential reduction of VQE optimization iterations.

Data contains personal data

No

Language

Method and outcome

Data format / data structure

Data collection
  • Mode of collection: Simulation
  • Description of the mode of collection: Simulation of quantum computer with and without simulated sampling of quantum state
  • Time period(s) for data collection: 2022 – 2023
Geographic coverage
Administrative information

Contributor(s)

Oskar Leinonen - University of Oslo orcid

Martin Rahm - Chalmers University of Technology, Department of Chemistry and Chemical Engineering orcid

Phalgun Lolur - National Quantum Computing Centre, UK Research and Innovation orcid

Mårten Skogh - AstraZeneca, Data Science and Modelling, Pharmaceutical Sciences, R&D, Gothenburg orcid

Funding

  • Funding agency: Wallenberg Center for Quantum Technology (WACQT)
Topic and keywords

Research area

Theoretical chemistry (Standard för svensk indelning av forskningsämnen 2011)

Atom and molecular physics and optics (Standard för svensk indelning av forskningsämnen 2011)

Other physics topics (Standard för svensk indelning av forskningsämnen 2011)

Physical chemistry (Standard för svensk indelning av forskningsämnen 2011)

Keywords

Electronic structure, Optimization, Quantum computing

Publications

Accelerating Variational Quantum Eigensolver Convergence using Parameter Transfer
: [Under review]

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Copyright

Copyright 2023 Mårten Skogh & Oskar Leinonen

License

CC BY 4.0

Versions

Version 1. 2023-07-26

Version 1: 2023-07-26

DOI: https://doi.org/10.5878/fvza-z272

Published: 2023-07-26