Efficient Ground State Energy Estimation of LiCoO2 Using the FMO-VQE Hybrid Quantum Algorithm

The Variational Quantum Eigensolver (VQE) is a quantum algorithm for estimating ground-state energies, with promising applications in material science, drug discovery, and battery research. A key challenge is the limited number of qubits available on current quantum devices, which restricts the size of molecular systems that can be studied. To address this limitation, we apply the Fragment Molecular Orbital (FMO) method in combination with VQE, referred to as FMO-VQE. This approach divides a system into smaller fragments, making the quantum calculations more tractable. While earlier studies demonstrated this method only for hydrogen clusters, we extend the application to lithium cobalt oxide, a widely used cathode material in lithium-ion batteries. Using FMO-VQE, we estimate the ground-state energy of this complex system while reducing the number of required qubits from 24 to 14, without significant loss of accuracy compared to classical methods. This reduction highlights the potential of FMO-VQE to overcome hardware limitations and make quantum simulations of larger molecules feasible. The results suggest a practical path for applying near-term quantum computers to real-world challenges, opening opportunities for advancements in the battery industry and drug design.

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Publication Details

Journal
Mathematics
Published
2025-12-22
DOI
https://doi.org/10.3390/math14010044
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Efficient Ground State Energy Estimation of LiCoO2 Using the FMO-VQE Hybrid Quantum Algorithm

D Kim, Younghun Kwon, Yoonho Choe
Mathematics
Machine Learning in Materials Science
article

Efficient Ground State Energy Estimation of LiCoO2 Using the FMO-VQE Hybrid Quantum Algorithm

D Kim, Younghun Kwon, Yoonho Choe
article en

Abstract

The Variational Quantum Eigensolver (VQE) is a quantum algorithm for estimating ground-state energies, with promising applications in material science, drug discovery, and battery research. A key challenge is the limited number of qubits available on current quantum devices, which restricts the size of molecular systems that can be studied. To address this limitation, we apply the Fragment Molecular Orbital (FMO) method in combination with VQE, referred to as FMO-VQE. This approach divides a system into smaller fragments, making the quantum calculations more tractable. While earlier studies demonstrated this method only for hydrogen clusters, we extend the application to lithium cobalt oxide, a widely used cathode material in lithium-ion batteries. Using FMO-VQE, we estimate the ground-state energy of this complex system while reducing the number of required qubits from 24 to 14, without significant loss of accuracy compared to classical methods. This reduction highlights the potential of FMO-VQE to overcome hardware limitations and make quantum simulations of larger molecules feasible. The results suggest a practical path for applying near-term quantum computers to real-world challenges, opening opportunities for advancements in the battery industry and drug design.

MathematicsVol. 14(1)
Hanyang University (KR)
National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Affordable and clean energy
Openalex Percentile: Top 49%
Machine Learning in Materials Science
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Efficient Ground State Energy Estimation of LiCoO2 Using the FMO-VQE Hybrid Quantum Algorithm — D Kim, Younghun Kwon, et al. · Mathematics (2025) | TGRS Research Map | TGRS