  With the birth of Noisy Intermediate Scale Quantum (NISQ) devices and the
verification of "quantum supremacy" in random number sampling and boson
sampling, more and more fields hope to use quantum computers to solve specific
problems, such as aerodynamic design, route allocation, financial option
prediction, quantum chemical simulation to find new materials, and the
challenge of quantum cryptography to automotive industry security. However,
these fields still need to constantly explore quantum algorithms that adapt to
the current NISQ machine, so a quantum programming framework that can face
multi-scenarios and application needs is required. Therefore, this paper
proposes QPanda, an application scenario-oriented quantum programming framework
with high-performance simulation. Such as designing quantum chemical simulation
algorithms based on it to explore new materials, building a quantum machine
learning framework to serve finance, etc. This framework implements
high-performance simulation of quantum circuits, a configuration of the fusion
processing backend of quantum computers and supercomputers, and compilation and
optimization methods of quantum programs for NISQ machines. Finally, the
experiment shows that quantum jobs can be executed with high fidelity on the
quantum processor using quantum circuit compile and optimized interface and
have better simulation performance.
