On the relation between trainability and dequantization of variational quantum learning models
Dahlem Center for Complex Quantum Systems, Freie Universität Berlin（弗里堡柏林自由大学复杂量子系统中心）；Lorentz Instituut, Universiteit Leiden（莱顿大学洛伦兹研究所）；LIACS, Universiteit Leiden（莱顿大学信息科学与计算研究所）
The quest for successful variational quantum machine learning (QML) relies on the design of suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classical machine learning. Successful QML models must fulfill the properties of trainability and non-dequantization, among others. Recent works have highlighted an intricate interplay between trainability and dequantization of such models, which is still unresolved. In this work we contribute to this debate from the perspective of machine learning, proving a number of results identifying, among others when trainability and non-dequantization are not mutually exclusive. We begin by providing a number of new somewhat broader definitions of the relevant concepts, compared to what is found in other literature, which are operationally motivated, and consistent with prior art. With these precise definitions given and motivated, we then study the relation between trainability and dequantization of variational QML. Next, we also discuss the degrees of "variationalness" of QML models, where we distinguish between models like the hardware efficient ansatz and quantum kernel methods. Finally, we introduce recipes for building PQC-based QML models which are both trainable and nondequantizable, and corresponding to different degrees of variationalness. We do not address the practical utility for such models. Our work however does point toward a way forward for finding more general constructions, for which finding applications may become feasible.
Beyond Logical Circuits: Hardware-Aware Analysis of Expressibility and Trainability in Variational Quantum Algorithms
Division of Engineering, New York University Abu Dhabi (NYUAD)（纽约大学阿布扎比分校工程学院）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD（纽约大学阿布扎比分校研究院量子与拓扑系统中心）
Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Imperial College London（帝国理工学院）；Jagiellonian University（雅盖隆大学）；Quantum Science Center（量子科学中心）；Center for Nonlinear Studies（非线性研究中心）
Late Breaking Results: Hardware-Aware Compilation Reshapes Trainability in Variational Quantum Circuits
eBRAIN Lab, Division of Engineering, New York University (NYU) Abu Dhabi（纽约大学阿布扎比分校工程学院）；Center for Quantum and Topological Systems, NYUAD Research Institute, NYU Abu Dhabi（纽约大学阿布扎比分校研究学院量子与拓扑系统中心）
Towards Surrogate Based Dequantization of Quantum Reinforcement Learning
University of the Basque Country UPV/EHU（巴斯克大学 UPV/EHU）；TECNALIA, Basque Research and Technology Alliance (BRTA)（TECNALIA，巴斯克研究与技术联盟（BRTA））；Freie Universität Berlin（柏林自由大学）；Helmholtz-Zentrum Berlin für Materialien und Energie（亥姆霍兹柏林材料与能源中心）；IKERBASQUE, Basque Foundation for Science（IKERBASQUE，巴斯克科学基金会）；Basque Center for Applied Mathematics (BCAM)（巴斯克应用数学中心（BCAM））；African Institute for Mathematical Sciences (AIMS)（非洲数学科学研究所（AIMS））；Stellenbosch University（斯泰伦博斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS)（国家理论与计算科学研究所（NITheCS））
Quantum machine learning for the quantum lattice Boltzmann method: Trainability of variational quantum circuits for the nonlinear collision operator across multiple time steps
Aix-Marseille University（艾克斯-马赛大学）；Quanscient Oy（Quanscient公司）；Faculty of Technical Sciences, University of Novi Sad（诺维萨德大学技术科学学院）；CNRS（法国国家科学研究中心）；M2P2 Laboratory（M2P2实验室）；Lappeenranta–Lahti University of Technology（拉彭兰塔-拉赫蒂理工大学）
Subtleties in the trainability of quantum machine learning models
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；National University of Singapore（新加坡国立大学）；Imperial College London（帝国理工学院）；State University of New York at Stony Brook（纽约州立大学石溪分校）
Variational quantum simulation: a case study for understanding warm starts
Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院物理研究所）；IBM Quantum, IBM Research – Zurich（IBM量子（IBM苏黎世研究院））；Chulalongkorn University（朱拉隆功大学）
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）
Trainability-Oriented Hybrid Quantum Regression via Geometric Preconditioning and Curriculum Optimization
Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence（上海交通大学-重庆人工智能研究院）；Department of Mathematics, National University of Singapore（新加坡国立大学数学系）
Quantum circuit architecture search for variational quantum algorithms
The University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）；SenseTime（商汤科技）；University of Technology Sydney（悉尼科技大学）；Hon Hai Quantum Computing Research Center（鸿海量子计算研究中心）
Entanglement geometry separates circuit cutting, classical hardness, and trainability
Quantum Software Lab（量子软件实验室）；University of Edinburgh, UK（爱丁堡大学）；IBM Research（IBM研究院）；Centre for Quantum Technologies（量子技术中心）；National University of Singapore（新加坡国立大学）
Potential and limitations of random Fourier features for dequantizing quantum machine learning
IBM Quantum（IBM量子计算部门）；Almaden Research Center（阿尔马登研究中心）；ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology（ICFO-光子科学研究所（巴塞罗那科学技术学院））；Eurecat, Centre Tecnologic de Catalunya（加泰罗尼亚技术中心Eurecat）；Freie Universität Berlin（柏林自由大学）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Helmholtz-Zentrum Berlin für Materialien und Energie（亥姆霍兹柏林材料与能源研究中心）
Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms
Chula Intelligent and Complex Systems Lab, Department of Physics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand, 10330（朱拉隆功大学科学学院物理系复杂系统实验室）；Siam Quantum Square, Faculty of Science, Chulalongkorn University, Bangkok, Thailand, 10330（朱拉隆功大学科学学院暹罗量子广场）；Centre for Quantum Science and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland（瑞士洛桑联邦理工学院量子科学与工程中心）
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Quantum Science Center（量子科学中心）；Google Quantum AI Team（谷歌量子人工智能团队）；University of Oxford（牛津大学）；NTT Corporation（日本电报电话公司）；Osaka University（大阪大学）；RIKEN（理化学研究所）；Peking University（北京大学）
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）
Warm Starts, Cold States: Exploiting Adiabaticity for Variational Ground-States
Institute of Physics, Ecole Polytechnique F\' e d\' e rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland；Centre for Quantum Science；Engineering, Ecole Polytechnique F\' e d\' e rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland；Barcelona Supercomputing Center, Plaça Eusebi G\"uell, 1-3, 08034 Barcelona, Spain；Institute for Theoretical Physics, ETH Zürich, 8093, Zürich, Switzerland
Variational decision diagrams for quantum-inspired machine learning applications
D-Wave Systems（D-Wave系统）；Zapata Computing Inc.（Zapata计算公司）；Grupo de Superconductividad y Nanotecnología, Departamento de Física, Universidad Nacional de Colombia（超导与纳米技术小组，物理系，哥伦比亚国家大学）
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）
Training-efficient density quantum machine learning
QC Ware；LIP6, CNRS, Sorbonne Université（LIP6、CNRS、索邦大学）；IRIF, CNRS - University of Paris（IRIF、CNRS-巴黎大学）；Indian Institute of Technology, Roorkee（印度理工学院拉尔克学院）
