Image classification is a fundamental problem in computer vision, and neural networks provide an effective solution. With the advancement of quantum technology, quantum neural networks have attracted a lot of attention. However, they are only suitable for low-dimensional data and require dimensionality reduction and quantum encoding. Two image classification methods have recently emerged: one employs PCA for dimensionality reduction and angular encoding, and the other integrates QNN into a CNN to improve performance. Despite numerous algorithms, the differences between them remain 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. As the number of classes increases, research on PCA-based quantum algorithms reveals the barren plateau problem of QNN, which is not suitable for multiple classes in a hybrid setting. Our proposed model combining traditional CNN with QNN addresses QNN’s multi-class training difficulties to some extent and achieves satisfactory classification results. Nevertheless, its accuracy remains below that of the top-performing CNN models. Furthermore, we investigate transfer learning in hybrid quantum neural network models and assess the performance of our models on the quantum hardware from IBM. In conclusion, quantum neural networks show promise but require further research and optimization, facing challenges ahead.

Recently, multiparty quantum key agreement (MQKA) protocols have gained increasing attention. Several MQKA protocols in the traveling mode have been proposed for higher key generation rates, which, however, are vulnerable to a collusive attack presented by Liu. In this paper, an analysis of Liu's collusive attacks on Cao et al.'s protocol is given, based on which an efficient MQKA protocol that requires only sequential communication of a single d-level quantum system is put forward. Because of the single d-level quantum system, our protocol has huge advantages in scalability and can be realized with the state-of-the-art technology. The scalability means that each additional participant is only required to perform a unitary operator that is comparatively simple, and the complexity of measurements remains independent of the number of participants. Our protocol can also resist Liu's collusive attacks due to the encrypted quantum channel and has an efficiency rate of 1/2N(N is the number of participants) that is much better than those of other quantum key agreement protocols. In addition, by using the proposed encoding method, a wide class of problems for which quantum information protocols based on multisystem entanglement can be mapped into much simpler ones involving only one system.