Papers › MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum...
MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework
Xusheng Xu, Jiangyu Cui, Zidong Cui, Runhong He, Qingyu Li, Xiaowei Li, Yanling Lin, Jiale Liu, Wuxin Liu, Jiale Lu, Maolin Luo, Chufan Lyu, Shijie Pan, Mosharev Pavel, Runqiu Shu, Jialiang Tang, Ruoqian Xu, Shu Xu, Kang Yang, Fan Yu, Qingguo Zeng, Haiying Zhao, Qiang Zheng, Junyuan Zhou, Xu Zhou, Yikang Zhu, Zuoheng Zou, Abolfazl Bayat, Xi Cao, Wei Cui, Zhendong Li, Guilu Long, Zhaofeng Su, Xiaoting Wang, Zizhu Wang, Shijie Wei, Re-Bing Wu, Pan Zhang, Man-Hong Yung
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections