  With the rapid development of quantum computing technology, we have entered
the era of noisy intermediate-scale quantum (NISQ) computers. Therefore,
designing quantum algorithms that adapt to the hardware conditions of current
NISQ devices and can preliminarily solve some practical problems has become the
focus of researchers. In this paper, we focus on quantum generative models in
the field of quantum machine learning, and propose a hybrid quantum-classical
normalizing flow (HQCNF) model based on parameterized quantum circuits. Based
on the ideas of classical normalizing flow models and the characteristics of
parameterized quantum circuits, we cleverly design the form of the ansatz and
the hybrid method of quantum and classical computing, and derive the form of
the loss function in the case that quantum computing is involved. We test our
model on the image generation problem. Experimental results show that our model
is capable of generating images of good quality. Compared with other quantum
generative models, such as quantum generative adversarial networks (QGAN), our
model achieves lower (better) Fr\'echet inception distance (FID) score, and
compared with classical generative models, we can complete the image generation
task with significantly fewer parameters. These results prove the advantage of
our proposed model.
