Image classification is a fundamental computer vision problem, and neural networks offer efficient solutions. With advancing quantum technology, quantum neural networks have gained attention. However, they work only for low-dimensional data and demand dimensionality reduction and quantum encoding. Two recent image classification methods have emerged: one employs PCA dimensionality reduction and angle encoding, the other integrates QNNs into CNNs to boost performance. Despite numerous algorithms, comparing PCA reduction with angle encoding against the latter remains unclear. This study explores these algorithms' performance in multi-class image classification and proposes an optimized hybrid quantum neural network suitable for the current environment. Investigating PCA-based quantum algorithms unveils a barren plateau issue for QNNs as categories increase, unsuitable for multi-class in the hybrid setup. Simultaneously, the combined CNN-QNN model partly overcomes QNN's multi-class training challenges but lags in accuracy to superior traditional CNN models. Additionally, this work explores transfer learning in the hybrid quantum neural network model. In conclusion, quantum neural networks show promise but require further research and optimization, facing challenges ahead.
A hybrid variational quantum circuit approach for stabilizer states classifiers
Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 6303 CNRS, University of Technology of Belfort-Montbéliard（贝尔福-蒙贝利亚尔技术大学）；Mathematics and Statistics Department, Auburn University（奥本大学）
Hybrid boson sampling-neural network architecture for enhanced classification
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿研究院）；Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China（电子科技大学量子物理与光子量子信息教育部重点实验室）；Shimmer Center, Tianfu Jiangxi Laboratory（天府江西实验室 shimmer 中心）
A scalable quantum-neural hybrid variational algorithm for ground state estimation
Seoul National University（首尔大学）；NextQuantum, Seoul National University（NextQuantum，首尔大学）；Automation and Systems Research Institute, Seoul National University（自动化与系统研究所，首尔大学）；Dept. of Computer Science and Engineering, Seoul National University（计算机科学与工程系，首尔大学）；Dept. of Electrical and Computer Engineering, Seoul National University（电气与计算机工程系，首尔大学）；Institute of Computer Technology, Seoul National University（计算机技术研究所，首尔大学）
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）
Hybrid Quantum-Classical Learning for Multiclass Image Classification
Department of Computer Science and Engineering, Brac University, Dhaka 1212, Bangladesh（计算机科学与工程系，布拉大学，达卡1212，孟加拉国）；Blackett Laboratory, Imperial College London, London SW7 2AZ, United Kingdom（布莱克特实验室，帝国理工学院伦敦分校，伦敦SW7 2AZ，英国）
Hybrid quantum-classical neural network for sample-efficient recognition of topological phases
Friedrich-Alexander Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；ETH Zurich（苏黎世联邦理工学院）；Quantum Center, ETH Zurich（苏黎世联邦理工学院量子中心）；Paul Scherrer Institute（保罗谢勒研究所）；Max Planck Institute for the Science of Light（马克斯·普朗克光科学研究所）；Quint Computing GmbH（Quint Computing有限公司）
Hybrid Quantum Neural Networks for Enhanced Breast Cancer Thermographic Classification: A Novel Quantum-Classical Integration Approach
Faculty of Computing（计算学院）；Universiti Teknologi Malaysia（技术大学）；Faculty of Science and Technology（科学与技术学院）；Universitas Muhammadiyah Sidoarjo（穆罕默德大学西多亚罗乔）；Department of Information System（信息系统系）；Universitas Siber Jakarta（西巴克大学）
