  With the rapid development of classical and quantum machine learning, a large
number of machine learning frameworks have been proposed. However, existing
machine learning frameworks usually only focus on classical or quantum, rather
than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new
generation of unified classical and quantum machine learning framework that
supports hybrid optimization. The core library of the framework is implemented
in C++, and the user level is implemented in Python, and it supports deployment
on quantum and classical hardware. In this article, we analyze the development
trend of the new generation machine learning framework and introduce the design
principles of VQNet 2.0 in detail: unity, practicality, efficiency, and
compatibility, as well as full particulars of implementation. We illustrate the
functions of VQNet 2.0 through several basic applications, including classical
convolutional neural networks, quantum autoencoders, hybrid classical-quantum
networks, etc. After that, through extensive experiments, we demonstrate that
the operation speed of VQNet 2.0 is higher than the comparison method. Finally,
through extensive experiments, we demonstrate that VQNet 2.0 can deploy on
different hardware platforms, the overall calculation speed is faster than the
comparison method. It also can be mixed and optimized with quantum circuits
composed of multiple quantum computing libraries.
