  We introduce MindSpore Quantum, a pioneering hybrid quantum-classical
framework with a primary focus on the design and implementation of noisy
intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of
MindSpore, an advanced open-source deep learning training/inference framework,
MindSpore Quantum exhibits exceptional efficiency in the design and training of
variational quantum algorithms on both CPU and GPU platforms, delivering
remarkable performance. Furthermore, this framework places a strong emphasis on
enhancing the operational efficiency of quantum algorithms when executed on
real quantum hardware. This encompasses the development of algorithms for
quantum circuit compilation and qubit mapping, crucial components for achieving
optimal performance on quantum processors. In addition to the core framework,
we introduce QuPack, a meticulously crafted quantum computing acceleration
engine. QuPack significantly accelerates the simulation speed of MindSpore
Quantum, particularly in variational quantum eigensolver (VQE), quantum
approximate optimization algorithm (QAOA), and tensor network simulations,
providing astonishing speed. This combination of cutting-edge technologies
empowers researchers and practitioners to explore the frontiers of quantum
computing with unprecedented efficiency and performance.
