Quantum federated learning has brought about the improvement of privacy image classification, while the lack of personality of the client model may contribute to the suboptimal of quantum federated learning. A personalized quantum federated learning algorithm for privacy image classification is proposed to enhance the personality of the client model in the case of an imbalanced distribution of images. First, a personalized quantum federated learning model is constructed, in which a personalized layer is set for the client model to maintain the personalized parameters. Second, a personalized quantum federated learning algorithm is introduced to secure the information exchanged between the client and server.Third, the personalized federated learning is applied to image classification on the FashionMNIST dataset, and the experimental results indicate that the personalized quantum federated learning algorithm can obtain global and local models with excellent performance, even in situations where local training samples are imbalanced. The server's accuracy is 100% with 8 clients and a distribution parameter of 100, outperforming the non-personalized model by 7%. The average client accuracy is 2.9% higher than that of the non-personalized model with 2 clients and a distribution parameter of 1. Compared to previous quantum federated learning algorithms, the proposed personalized quantum federated learning algorithm eliminates the need for additional local training while safeguarding both model and data privacy.It may facilitate broader adoption and application of quantum technologies, and pave the way for more secure, scalable, and efficient quantum distribute machine learning solutions.

翻译：量子联邦学习已为隐私图像分类带来改进，然而客户端模型缺乏个性化可能导致量子联邦学习性能欠佳。本文提出一种面向隐私图像分类的个性化量子联邦学习算法，以在图像分布不均衡情况下增强客户端模型的个性化特性。首先，构建个性化量子联邦学习模型，其中为客户端模型设置个性化层以维护个性化参数。其次，引入个性化量子联邦学习算法以保障客户端与服务器间交换信息的安全性。第三，将个性化联邦学习应用于FashionMNIST数据集的图像分类任务，实验结果表明：即使在本地训练样本分布不均衡的情况下，所提个性化量子联邦学习算法仍能获得性能优异的全局与局部模型。在8个客户端且分布参数为100的设置下，服务器准确率达到100%，较非个性化模型提升7%；在2个客户端且分布参数为1的设置下，平均客户端准确率较非个性化模型提高2.9%。相较于现有量子联邦学习算法，所提算法在保障模型与数据隐私的同时，无需额外本地训练。该研究有望促进量子技术的更广泛采纳与应用，并为构建更安全、可扩展、高效的量子分布式机器学习解决方案开辟道路。