Exploring VQE for Ground State Energy Calculations of Small Molecules With Higher Bond Orders

In this research paper, we explore the application of quantum computing technologies, specifically the Variational Quantum Eigensolver, to accurately determine the ground state energies of small molecules, especially for molecules with higher bond orders. Our study showcases the feasibility and accuracy of quantum algorithms in performing complex quantum mechanical calculations for molecules like hydrogen, neon, water, methane, nitrogen, oxygen, ethyne, and ethene. Our results demonstrate high precision and reliability in molecular simulations with errors from theoretical values of less than 2 percent for most of the molecules. We also address the challenges associated with scalability and error rates when dealing with larger and more complex molecules. By combining the power of quantum computing with molecular simulations, we aim to enhance our understanding of molecular behavior, leading to more precise predictions in material science and drug discovery.

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Published
2024-09-04
DOI
https://doi.org/10.1109/globalaisummit62156.2024.10947806
Primary Topic
Machine Learning in Materials Science
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article
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article

Exploring VQE for Ground State Energy Calculations of Small Molecules With Higher Bond Orders

Ashwath Babu H, Anagha S Setlur, Vidya Niranjan, Akshay Uttarkar et al.
Machine Learning in Materials Science
article

Exploring VQE for Ground State Energy Calculations of Small Molecules With Higher Bond Orders

Ashwath Babu H, Anagha S Setlur, Vidya Niranjan, Akshay Uttarkar, S Rutwik
article en

Abstract

In this research paper, we explore the application of quantum computing technologies, specifically the Variational Quantum Eigensolver, to accurately determine the ground state energies of small molecules, especially for molecules with higher bond orders. Our study showcases the feasibility and accuracy of quantum algorithms in performing complex quantum mechanical calculations for molecules like hydrogen, neon, water, methane, nitrogen, oxygen, ethyne, and ethene. Our results demonstrate high precision and reliability in molecular simulations with errors from theoretical values of less than 2 percent for most of the molecules. We also address the challenges associated with scalability and error rates when dealing with larger and more complex molecules. By combining the power of quantum computing with molecular simulations, we aim to enhance our understanding of molecular behavior, leading to more precise predictions in material science and drug discovery.

Indraprastha Institute of Information Technology Delhi (IN), Indian Institute of Technology Dharwad (IN), Indian Institute of Information Technology Dharwad, Visvesvaraya Technological University (IN)
Affordable and clean energy
Openalex Percentile: Top 82%
Machine Learning in Materials Science
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Exploring VQE for Ground State Energy Calculations of Small Molecules With Higher Bond Orders — Ashwath Babu H, Anagha S Setlur, et al. · (2024) | TGRS Research Map | TGRS