  Deep learning is a modern approach to realize artificial intelligence. Many
frameworks exist to implement the machine learning task; however, performance
is limited by computing resources. Using a quantum computer to accelerate
training is a promising approach. The variational quantum circuit (VQC) has
gained a great deal of attention because it can be run on near-term quantum
computers. In this paper, we establish a new framework that merges traditional
machine learning tasks with the VQC. Users can implement a trainable quantum
operation into a neural network. This framework enables the training of a
quantum-classical hybrid task and may lead to a new area of quantum machine
learning.
