Diffusion-Enhanced Optimization of Variational Quantum Eigensolver for General Hamiltonians

Shikun Zhang; Zheng Qin; Yongyou Zhang; Yang Zhou; Rui Li; Chunxiao Du; Zhisong Xiao

doi:10.1002/qute.202500766

Diffusion-Enhanced Optimization of Variational Quantum Eigensolver for General Hamiltonians

Shikun Zhang
, Zheng Qin
, Yongyou Zhang
, Yang Zhou^*
, Rui Li
, Chunxiao Du
, Zhisong Xiao

^*此作品的通讯作者

Beihang University
Henan University
Beijing Institute of Technology
Beijing Information Science & Technology University

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

Variational quantum algorithms (VQAs) have emerged as a promising approach for achieving quantum advantage on current noisy intermediate-scale quantum devices. However, their large-scale applications are significantly hindered by optimization challenges, such as the barren plateau (BP) phenomenon, local minima, and numerous iteration demands. In this work, we leverage denoising diffusion models (DM) to address these difficulties. The DM is trained on a few data points in the Heisenberg model parameter space and then can be guided to generate high-performance parameters for parameterized quantum circuits (PQCs) in variational quantum eigensolver (VQE) tasks for general Hamiltonians. Numerical experiments demonstrate that DM-parameterized VQE can explore the ground-state energies of Heisenberg models with parameters not included in the training dataset. Even when applied to previously unseen Hamiltonians, such as the Ising and Hubbard models, it can generate the appropriate initial state to achieve rapid convergence and mitigate the BP and local minima problems. More interestingly, we discover the possibility of parameter transferability and extrapolation among different quantum many-body Hamiltonians.

源语言	英语
文章编号	e00766
期刊	Advanced Quantum Technologies
卷	9
期	2
DOI	https://doi.org/10.1002/qute.202500766
出版状态	已出版 - 2月 2026
已对外发布	是

访问文件

10.1002/qute.202500766

其它文件与链接

链接到 Scopus 的出版物

引用此

@article{6278ccc21b82411599632d5f9f6d2c10,

title = "Diffusion-Enhanced Optimization of Variational Quantum Eigensolver for General Hamiltonians",

abstract = "Variational quantum algorithms (VQAs) have emerged as a promising approach for achieving quantum advantage on current noisy intermediate-scale quantum devices. However, their large-scale applications are significantly hindered by optimization challenges, such as the barren plateau (BP) phenomenon, local minima, and numerous iteration demands. In this work, we leverage denoising diffusion models (DM) to address these difficulties. The DM is trained on a few data points in the Heisenberg model parameter space and then can be guided to generate high-performance parameters for parameterized quantum circuits (PQCs) in variational quantum eigensolver (VQE) tasks for general Hamiltonians. Numerical experiments demonstrate that DM-parameterized VQE can explore the ground-state energies of Heisenberg models with parameters not included in the training dataset. Even when applied to previously unseen Hamiltonians, such as the Ising and Hubbard models, it can generate the appropriate initial state to achieve rapid convergence and mitigate the BP and local minima problems. More interestingly, we discover the possibility of parameter transferability and extrapolation among different quantum many-body Hamiltonians.",

keywords = "barren plateau, diffusion model, parameter initialization, variational quantum eigensolver",

author = "Shikun Zhang and Zheng Qin and Yongyou Zhang and Yang Zhou and Rui Li and Chunxiao Du and Zhisong Xiao",

note = "Publisher Copyright: {\textcopyright} 2026 Wiley-VCH GmbH.",

year = "2026",

month = feb,

doi = "10.1002/qute.202500766",

language = "English",

volume = "9",

journal = "Advanced Quantum Technologies",

issn = "2511-9044",

publisher = "John Wiley \& Sons Inc.",

number = "2",

}

TY - JOUR

T1 - Diffusion-Enhanced Optimization of Variational Quantum Eigensolver for General Hamiltonians

AU - Zhang, Shikun

AU - Qin, Zheng

AU - Zhang, Yongyou

AU - Zhou, Yang

AU - Li, Rui

AU - Du, Chunxiao

AU - Xiao, Zhisong

PY - 2026/2

Y1 - 2026/2

N2 - Variational quantum algorithms (VQAs) have emerged as a promising approach for achieving quantum advantage on current noisy intermediate-scale quantum devices. However, their large-scale applications are significantly hindered by optimization challenges, such as the barren plateau (BP) phenomenon, local minima, and numerous iteration demands. In this work, we leverage denoising diffusion models (DM) to address these difficulties. The DM is trained on a few data points in the Heisenberg model parameter space and then can be guided to generate high-performance parameters for parameterized quantum circuits (PQCs) in variational quantum eigensolver (VQE) tasks for general Hamiltonians. Numerical experiments demonstrate that DM-parameterized VQE can explore the ground-state energies of Heisenberg models with parameters not included in the training dataset. Even when applied to previously unseen Hamiltonians, such as the Ising and Hubbard models, it can generate the appropriate initial state to achieve rapid convergence and mitigate the BP and local minima problems. More interestingly, we discover the possibility of parameter transferability and extrapolation among different quantum many-body Hamiltonians.

AB - Variational quantum algorithms (VQAs) have emerged as a promising approach for achieving quantum advantage on current noisy intermediate-scale quantum devices. However, their large-scale applications are significantly hindered by optimization challenges, such as the barren plateau (BP) phenomenon, local minima, and numerous iteration demands. In this work, we leverage denoising diffusion models (DM) to address these difficulties. The DM is trained on a few data points in the Heisenberg model parameter space and then can be guided to generate high-performance parameters for parameterized quantum circuits (PQCs) in variational quantum eigensolver (VQE) tasks for general Hamiltonians. Numerical experiments demonstrate that DM-parameterized VQE can explore the ground-state energies of Heisenberg models with parameters not included in the training dataset. Even when applied to previously unseen Hamiltonians, such as the Ising and Hubbard models, it can generate the appropriate initial state to achieve rapid convergence and mitigate the BP and local minima problems. More interestingly, we discover the possibility of parameter transferability and extrapolation among different quantum many-body Hamiltonians.

KW - barren plateau

KW - diffusion model

KW - parameter initialization

KW - variational quantum eigensolver

UR - https://www.scopus.com/pages/publications/105029533713

U2 - 10.1002/qute.202500766

DO - 10.1002/qute.202500766

M3 - Article

AN - SCOPUS:105029533713

SN - 2511-9044

VL - 9

JO - Advanced Quantum Technologies

JF - Advanced Quantum Technologies

IS - 2

M1 - e00766

ER -

Diffusion-Enhanced Optimization of Variational Quantum Eigensolver for General Hamiltonians

摘要

访问文件

其它文件与链接

学术指纹

引用此