Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum-classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analysis how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. As a result, the model can be trained in an end-to-end fashion, analogue to the backpropagation in classical neural networks.
To explore the effectiveness of Res-HQCNN , we perform extensive experiments for quantum data with or without noisy on classical computer. The experimental results show the Res-HQCNN performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts. Moreover, the possible methods of combining residual learning with quantum neural networks are also discussed.
Readout-Side Bypass for Residual Hybrid Quantum-Classical Models
Department of Engineering Management and Systems Engineering, George Washington University, USA（工程管理与系统工程系，乔治华盛顿大学）；Department of Computer Science, University of Warwick, UK（计算机科学系，沃林汉姆大学）；Department Computer Science, Kent State University, USA（计算机科学系，肯特州立大学）；Department of Computer Science, Information, and Engineering Technology, Youngstown State University, USA（计算机科学、信息与工程技术系，扬斯敦州立大学）
Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognition
Institute for Brain Sciences（脑科学研究院）；Kuang Yaming Honors School, Nanjing University（南京大学匡杨荣誉学院）；National Laboratory of Solid State Microstructures（固态微结构国家实验室）；School of Physics, Collaborative Innovation Center of Advanced Microstructures（物理学院，先进微结构协同创新中心）；School of Physics（物理学院）；Beijing Key Laboratory of Opto-Electronic Functional Materials（北京光学材料重点实验室）；Micro-Nano Devices, Key Laboratory of Quantum State Construction（微纳器件，量子态构造重点实验室）；Manipulation(Ministry of Education), Renmin University of China（操控（教育部），中国人民大学）
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning
KTH Royal Institute of Technology（KTH皇家理工学院）；Jeonbuk National University（国立全北大学）；Hanyang University（汉阳大学）；Institute of Nano Science & Technology, Hanyang University（汉阳大学纳米科学与技术研究所）
Residual GRU+MHSA: A Lightweight Hybrid Recurrent Attention Model for Cardiovascular Disease Detection
George Mason University（乔治·马歇尔大学）；University of Southern Queensland（南方昆士兰大学）；University of Aveiro（阿维罗大学）；Edinburgh Napier University（爱丁堡纳皮尔大学）；Northwood High School（北伍德高中）
Hybrid Multi-Dimensional MRI Prostate Cancer Detection via Hadamard Network-Based Bias Correction and Residual Networks
Electrical and Computer Engineering Department, University of Illinois Chicago（伊利诺伊大学芝加哥分校电子与计算机工程系）；Machine and Hybrid Intelligence Lab, Northwestern University（西北大学机器与混合智能实验室）；Department of Radiology, University of Chicago（芝加哥大学放射学系）
RINS: Residual-Image Neural Subspace Solvers for Large Sparse Linear Systems
Shanghai Innovation Institute（上海创新研究院）；Harbin Institute of Technology（哈尔滨工业大学）；Shanghai Jiao Tong University（上海交通大学）；Renmin University of China（中国人民大学）
XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations
Division of Advanced Nuclear Engineering, Pohang University of Science and Technology（浦项国立科学技术大学先进核工程系）；Division of Applied Mathematics, Brown University（布朗大学应用数学系）；Graduate School of Integrated Energy-AI, Jeonbuk National University（全北国立大学整合能源-人工智能研究生院）
From Classical to Hybrid: A Practical Framework for Quantum-Enhanced Learning
Gran Sasso Science Institute（格拉斯索科学研究所）；Technical University of Munich（慕尼黑技术大学）；VSB - Technical University of Ostrava（奥斯特拉瓦技术大学）；IT4Innovations National Supercomputing Center（IT4Innovations国家超级计算中心）；Klaipeda University（克莱佩达大学）；International Centre for Genetic Engineering and Biotechnology（国际基因工程与生物技术中心）
Diversity-Aware Adaptive Collocation for Physics-Informed Neural Networks via Sparse QUBO Optimization and Hybrid Coresets
Research Center for Artificial Intelligence（人工智能研究中心）；Innopolis University（因诺波利斯大学）；Moscow Center for Advanced Studies（莫斯科先进研究中心）；Laboratory of Quantum Computing（量子计算实验室）；Center for Engineering Systems and Sciences（工程系统与科学中心）；Central University（中央大学）；The Center for Top-Level Educational Programs in AI（人工智能顶级教育项目中心）；National Medical Research Center of Oncology（肿瘤医学研究中心）
Are Deep Learning Based Hybrid PDE Solvers Reliable? Why Training Paradigms and Update Strategies Matter
Delft Institute of Applied Mathematics（代尔夫特应用数学研究所）；Delft University of Technology（代尔夫特理工大学）；Department of Mathematics and Computer Science（数学与计算机科学系）；Eindhoven University of Technology（埃因霍温理工大学）；The Netherlands（荷兰）
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack
Clemson University（克莱姆森大学）；University of South Carolina（南卡罗来纳大学）；South Carolina State University（南卡罗来纳州立大学）；Benedict College（贝内迪克特学院）；The Citadel（城堡军事学院）
Modeling Nonlinear Oscillator Networks Using Physics-Informed Hybrid Reservoir Computing
University of Bristol, School of Computer Science（布里斯托大学计算机科学学院）；University of Bristol, School of Engineering Mathematics and Technology（布里斯托大学工程数学与技术学院）
