  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.
