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MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework

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arxiv 2406.17248 v3 pith:MPKNPQ2P submitted 2024-06-25 quant-ph

Xusheng Xu , Jiangyu Cui , Zidong Cui , Runhong He , Qingyu Li , Xiaowei Li , Yanling Lin , Jiale Liu
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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
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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.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ORBIT-Q: Dual-axis benchmarking of autonomous agents in scientific quantum programming

    quant-ph 2026-07 conditional novelty 6.5 of 10

    A dual-axis agent–framework benchmark finds TensorCircuit-NG and Codex with GPT-5.5 strongest on research quantum workflows, yet agent artifacts remain slower and less complete than expert TC code.

  2. Fock space prethermalization and time-crystalline order on a quantum processor

    quant-ph 2025-10 conditional novelty 6.0 of 10

    Strong Ising interactions approximately conserve domain-wall number, splitting Fock space into sparse sectors and yielding a long-lived discrete time crystal in a clean 72-qubit Floquet system.

  3. Universal Quantum Computational Spectroscopy on a Quantum Chip

    quant-ph 2025-06 conditional novelty 6.0 of 10

    A quantum auto-correlation function integrated over an extra time variable yields eigenenergies and eigenstate observables for Hermitian, non-Hermitian, and Floquet systems, demonstrated on a silicon-photonic chip.

  4. Simulating fluid vortex interactions on a superconducting quantum processor

    quant-ph 2025-06 conditional novelty 6.0 of 10

    A data-driven quantum vortex method with spatiotemporal encoding simulates leapfrogging vortex interactions on an 8-qubit superconducting processor, using an evolution operator fitted to classical simulation data.

  5. A Quantum Annealing Approach for Solving Optimal Feature Selection and Next Release Problems

    cs.SE 2025-06 conditional novelty 5.0 of 10

    Quantum annealing combined with weighted objectives and problem decomposition finds competitive non-dominated solutions to next-release and feature-selection problems faster than NSGA-II in most tested cases.

  6. Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection

    quant-ph 2025-06 conditional novelty 5.0 of 10

    Quantum phase transition locations can be inferred from VQE-optimized circuit parameters using an unsupervised attention-VAE, with a data-driven generalized order parameter.

  7. Optimal Control by Variational Quantum Algorithms

    quant-ph 2025-05 conditional novelty 5.0 of 10

    The paper shows numerically that a variational quantum algorithm with an uncorrelated ansatz transfers a spin excitation across a chain in time close to the (N-1)/(2J0) bound, and proposes a W1-based control optimalit...

  8. Distributed Exact Quantum Amplitude Amplification Algorithm for Arbitrary Quantum States

    quant-ph 2026-01 conditional novelty 4.0 of 10

    A distributed exact amplitude amplification algorithm is presented and simulated on 4–10 qubits, reporting large gate/depth reductions over centralized versions.

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