Are quantum advantages in discrete manufacturing achievable in the near term? As manufacturing-relevant NISQ algorithms, we identified Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) for combinatorial optimization as well as Derivative Quantum Circuits (DQC) for solving non-linear PDEs. While there is evidence for QAOA's outperformance, this requires post-NISQ circuit depths. In the case of QA, there is up to now no unquestionable evidence for advantage compared to classical computation. Yet different protocols could lead to finding such instances. Together with a well-chosen quantum feature map, DQC are a promising concept. Further investigations for higher dimensional problems and improvements in training could follow.
Quantum annealing inspired algorithms for the NISQ Era
Jülich Supercomputing Centre, Forschungszentrum Jülich（于利希超级计算中心，于利希研究中心）；RWTH Aachen University（亚琛工业大学）；Modular Supercomputing and Quantum Computing, Goethe University Frankfurt（法兰克福歌德大学模块化超算与量子计算）；Department of Computer Science, University of Cologne（科隆大学计算机科学系）
Quantum Simulation of Magnetic Materials: from Ab-Initio to NISQ
HQS Quantum Simulations GmbH（HQS量子模拟有限公司）；Department of High Performance Computing, Institute of Software Technology, German Aerospace Center (DLR)（德国航空航天中心软件技术研究所高性能计算部）
Quantum artificial vision for defect detection in manufacturing
Multiverse Computing；Ikerlan Technology Research Centre（Ikerlan技术研究中心）；Donostia International Physics Center（多诺斯蒂亚国际物理中心）；Ikerbasque Foundation for Science（Ikerbasque科学基金会）
From GDSII to Wafer: EDA Design Flow and Data Conversion for Wafer-Scale Manufacturing of Superconducting Quantum Chips
Yangtze Delta Industrial Innovation Center of Quantum Science and Technology（量子科学与技术长三角产业创新中心）；China Academy of Electronics and Information Technology（中国电子科技集团公司）
Optimized QUBO formulation methods for quantum computing
Scuola Normale Superiore（比萨高等师范学院）；QuSoft；Korteweg–de Vries Institute for Mathematics, University of Amsterdam（阿姆斯特丹大学科特韦格-德弗里斯数学研究所）；NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR（NEST，比萨高等师范学院与意大利国家研究委员会纳米科学研究所）
Quantum Computing and Error Mitigation with Deep Learning for Frenkel Excitons
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；IBM Research Almaden Lab（IBM阿尔马登研究中心）；Materials Research Laboratory, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校材料研究实验室）；National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校国家超级计算应用中心）
Quantum Kernel Methods: Convergence Theory, Separation Bounds and Applications to Marketing Analytics
Facultat Economia i Empresa, Universitat de Barcelona（巴塞罗那大学经济学院）；Department of Law and Economics, University Mediterranea of Reggio Calabria（雷焦卡拉布里亚地中海大学法律与经济学系）
Resource-Efficient Bio-Molecular Docking on a NISQ-era Digital Quantum Computer
Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR)（生物医学研究所，新加坡科技研究局）；A*STAR Quantum Innovation Centre (Q. InC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局量子创新中心）；Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR)（高级智能与计算研究所，新加坡科技研究局）；Centre for Quantum Technologies, National University of Singapore（新加坡国立大学量子技术中心）；Department of Physics, National University of Singapore（新加坡国立大学物理系）
Efficient Operator Selection and Warm-Start Strategy for Excitations in Variational Quantum Eigensolvers
ICAMS, Ruhr University Bochum（鲁尔大学波鸿校区计算物理与材料科学研究所）；German Aerospace Center (DLR), Institute for Frontier Materials on Earth and in Space（德国航空航天中心前沿地球与空间材料研究所）
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks
William & Mary（威廉与玛丽学院）
变分量子算法（VQA）最近受到了研究界的极大关注，因为它们在有噪声的中间尺度量子计算机（NISQ）中有很好的性能。然而，在具有随机初始化参数的参数化量子线路（PQC）上运行的VQA的特征在于贫瘠高原（BP），其中梯度在量子比特的数量上呈指数地消失。在本文中，我们首先回顾量子自然梯度（QNG），这是最流行的算法之一，在VQA，从经典的一阶优化的角度来看。然后，我们提出了一个\underline{L} ook \underline{A} round \underline{W} arm- \underline{S} tart QNG（LAWS）算法，以减轻广泛存在的BP问题。LAWS是一种组合优化策略，它利用了模型参数初始化和QNG快速收敛的优点。LAWS重复地重新调整参数搜索空间，以用于下一次迭代参数更新。重新初始化的参数搜索空间是通过对接近当前最优的梯度进行采样来仔细选择的。此外，我们提出了一个统一的框架（WS-SGD）集成参数初始化技术到优化器。我们提供了收敛性证明的建议框架的凸和非凸目标函数的基础上Polyak-Lojasiewicz（PL）条件。实验结果表明，该算法在量子分类问题中能有效地降低BP算法的泛化能力。
Development of Neural Network-Based Optimal Control Pulse Generator for Quantum Logic Gates Using the GRAPE Algorithm in NMR Quantum Computer
Sharif University of Technology（谢里夫理工大学）；Centre for Quantum Engineering and photonics Technology, Sharif University of Technology（谢里夫理工大学量子工程与光子技术中心）；Koc University（科奇大学）
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）
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）
Variational Quantum Classifiers Through the Lens of the Hessian
National Institute of Technology, Agartala（阿加尔塔拉国家理工学院）；Chitkara University Institute of Engineering & Technology, Chitkara University（奇特卡拉大学工程与技术学院）；Mansoura University（曼苏拉大学）
