Determining the ground state of a many-body Hamiltonian is a central problem across physics, chemistry, and combinatorial optimization, yet it is often classically intractable due to the exponential growth of Hilbert space with system size. Even on fault-tolerant quantum computers, quantum algorithms with convergence guarantees -- such as quantum phase estimation and quantum subspace methods -- require an initial state with sufficiently large overlap with the true ground state to be effective. Variational quantum eigensolvers (VQEs) are natural candidates for preparing such states; however, standard VQEs typically exhibit poor generalization, requiring retraining for each Hamiltonian instance, and often suffer from barren plateaus, where gradients can vanish exponentially with circuit depth and system size. To address these limitations, we propose an end-to-end representation learning framework that combines a graph autoencoder with a classical neural network to generate VQE parameters that generalize across Hamiltonian instances. By encoding interaction topology and coupling structure, the proposed model produces high-overlap initial states without instance-specific optimization. Through extensive numerical experiments on families of one- and two-local Hamiltonians, we demonstrate improved generalization and trainability, manifested as reduced test error and a significantly milder decay of gradient variance. We further show that our method substantially accelerates convergence in quantum subspace-based eigensolvers, highlighting its practical impact for downstream quantum algorithms.
Benchmarking Encoding Families in Quantum Neural Networks Under Fixed Circuit Area for Frequency Spectrum and Trainability
Military University of Technology（军事理工大学）；The Washington Institute for STEM Entrepreneurship and Research（华盛顿STEM创业与研究学院）；Fraunhofer Institute for Industrial Mathematics ITWM（弗劳恩霍夫工业数学研究所ITWM）；Georgia Institute of Technology（佐治亚理工学院）
Improving the trainability of VQE on NISQ computers for solving portfolio optimization using convex interpolation
Origin Quantum Computing Company Limited（本源量子计算科技有限责任公司）；Laboratory of Quantum Information, University of Science and Technology of China（中国科学技术大学量子信息实验室）；Department of Electronic Information and Artificial Intelligence, West Anhui University（皖西大学电子信息与人工智能学院）；College of Physics and Optoelectronic Engineering, Ocean University of China（中国海洋大学物理与电子工程学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Anhui Province Key Laboratory of Quantum Network, University of Science and Technology of China（中国科学技术大学安徽省量子网络重点实验室）
Can Chemically Inspired Parameter Initialization Mitigate Barren Plateaus in Variational Quantum Eigensolvers?
Guangdong Basic Research Center of Excellence for Aggregate Science, School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（广东聚合科学卓越研究中心，科学与工程学部，香港中文大学（深圳））；School of Physical and Chemical Sciences, Queen Mary University of London（物理与化学科学学院，伦敦大学玛丽女王学院）
Enhanced Distributed Variational Quantum Eigensolver for Large-Scale MaxCut Problem
Shenzhen SpinQ Technology Co., Ltd（深圳 spinq 科技有限公司）；College of Physics and Electronic Engineering, Center for Computational Sciences, Sichuan Normal University（四川师范大学物理与电子工程学院计算科学中心）；State Key Laboratory of Genome and Multi-omics Technologies, BGI Research（华大基因研究院基因组与多组学技术国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
From Barren Plateaus to SPSA Optimization in Variational Quantum Eigensolvers
Michigan Institute for Computational Discovery and Engineering, Department of Electrical Engineering and Computer Science and Department of Statistics, University of Michigan（密歇根大学计算发现与工程研究所、电气工程和计算机科学系以及统计学系）
Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver
National Institute of Advanced Industrial Science and Technology（日本先进工业科学技术研究所）；NVIDIA Corporation（英伟达公司）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；DENSO CORPORATION（电装公司）
Quantum Eigensolver for Non-Normal Matrices via Ground State Energy Estimation
Institute of Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）；State Key Laboratory of Mathematical Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院数学科学国家重点实验室）
Warm Start of Variational Quantum Algorithms for Quadratic Unconstrained Binary Optimization Problems
Deutsches Elektronen-Synchrotron DESY（德国电子同步加速器）；Computation-Based Science and Technology Research Center, The Cyprus Institute（塞浦路斯研究所计算科学与技术研究中心）；Institut für Physik, Humboldt-Universität zu Berlin（柏林洪堡大学物理研究所）；Institute for Quantum Computing Analytics (PGI-12) Jülich Research Center（于利希研究中心量子计算分析研究所）
Demonstration of subspace-search variational quantum eigensolver using photonic orbital angular momentum qudits
Korea Institute of Science and Technology (KIST)（韩国科学技术院）；Korea University of Science and Technology（韩国科学技术院）；Kyung Hee University（庆熙大学）；Gwangju Institute of Science and Technology（光州科学技术院）
Enhancing Pauli Correlation Encoding for quantum optimization via systematic expressivity analysis
Graduate School of Engineering Science, The University of Osaka（大阪大学工学研究科）；Center for Quantum Information and Quantum Biology, The University of Osaka（大阪大学量子信息与量子生物学中心）；RIKEN Center for Quantum Computing (RQC)（理化学研究所量子计算中心）；Graduate School of Informatics, Kyoto University（京都大学情报学府）
Efficient Lattice Hamiltonian Encoding for the Shortest Vector Problem
Imperial College London（帝国理工学院）；Raymond and Beverly Sackler School of Physics and Astronomy, Tel-Aviv University（特拉维夫大学雷蒙德和贝弗利萨克勒物理与天文学学院）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿罗森多夫亥姆霍兹中心）
All-valid-state HOBO encoding for constrained combinatorial optimization on NISQ devices
Tokyo University of Agriculture and Technology（东京农工大学）；The Institute of Medical Science, The University of Tokyo（东京大学医学科学研究所）；The University of Tokyo（东京大学）
