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Acta Physica Sinica

Quantum artificial intelligence: a review of the bidirectional empowerment mechanisms and frontier progress in AI and quantum computing

Junxia Yang, Qizhi Cai, Jinrong Guo, Guangwei Deng

Published Jan 1, 2026• 0 citations

Abstract

经典计算正逼近物理极限，而含噪声的中等规模量子硬件面临校准与控制瓶颈，这催化了量子人工智能这一前沿交叉领域的诞生。量子人工智能旨在利用人工智能与量子计算的协同作用，解决彼此的瓶颈问题。本综述系统性地分析量子人工智能的双向赋能框架。在“AI for Quantum”方面，AI正成为克服中等规模量子硬件瓶颈的工具，关键应用包括设备自动化校准与高保真读出、开发超越传统算法的AI量子纠错解码器及AI驱动的量子线路优化。在“Quantum for AI”方面，量子计算正为 AI 算法提供新的计算范式与模型设计思路，从早期的HHL、QSVM算法演进到中等规模量子时代主流的变分量子算法和量子神经网络，同时综述了该领域面临的贫瘠高原等可训练性挑战及其缓解策略，并涵盖了量子优化及量子原生智能模型。总结指出，量子人工智能的双向融合有望成为推动量子计算从中等规模量子时代迈向容错计算、构建下一代混合量子-经典智能系统的重要路径之一。

DOI: 10.7498/aps.75.20251792

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经典计算正逼近物理极限，而含噪声的中等规模量子硬件面临校准与控制瓶颈，这催化了量子人工智能这一前沿交叉领域的诞生。量子人工智能旨在利用人工智能与量子计算的协同作用，解决彼此的瓶颈问题。本综述系统性地分析量子人工智能的双向赋能框架。在“AI for Quantum”方面，AI正成为克服中等规模量子硬件瓶颈的工具，关键应用包括设备自动化校准与高保真读出、开发超越传统算法的AI量子纠错解码器及AI驱动的量子线路优化。在“Quantum for AI”方面，量子计算正为 AI 算法提供新的计算范式与模型设计思路，从早期的HHL、QSVM算法演进到中等规模量子时代主流的变分量子算法和量子神经网络，同时综述了该领域面临的贫瘠高原等可训练性挑战及其缓解策略，并涵盖了量子优化及量子原生智能模型。总结指出，量子人工智能的双向融合有望成为推动量子计算从中等规模量子时代迈向容错计算、构建下一代混合量子-经典智能系统的重要路径之一。

The intersection of artificial intelligence (AI) and quantum mechanics represents a frontier of scientific exploration, offering the potential to revolutionize our understanding of quantum systems. Despite the promise, significant challenges remain in effectively integrating AI techniques within quantum mechanics frameworks. This study aims to investigate the applications of AI in quantum mechanics, identifying both the challenges and opportunities presented by this interdisciplinary approach. The focus is on understanding how AI can enhance quantum simulations, optimize computations, and improve experimental designs. A comprehensive literature review was conducted, analyzing recent advancements in AI algorithms applied to quantum mechanics. Case studies were examined to illustrate successful implementations and the limitations encountered. Key metrics for evaluation included computational efficiency, accuracy, and scalability. Findings indicate that AI techniques, particularly machine learning and neural networks, can significantly expedite quantum simulations and enhance predictive accuracy. However, challenges such as data sparsity, interpretability of AI models, and the integration of AI with quantum algorithms were identified as significant barriers to progress. This research highlights the transformative potential of AI in advancing quantum mechanics while acknowledging the inherent challenges. Addressing these challenges will require collaborative efforts across disciplines, paving the way for innovative solutions that leverage AI to deepen our understanding of quantum phenomena and improve technological applications.

量子人工智能作为新兴学科分支，其研究范畴和应用场景都停留在理论上的构思之中，客观分辨这两个特征属性，以及从工程哲学角度分析其未来工程特征，是非常必要的。这种分析，是对整个量子人工智能学科所涉及的基础科学、技术和工程三个维度进行深入研究后得出的。由于科技的发展和综合国力的提高，我国更多地面临开创具有探索性特质的人类科技前沿领域的工程。从工程哲学视域出发，反思当下量子人工智能工程，从工程哲学的高度思考这一探索性的高科技工程的性质特征，并且通过对量子人工智能工程可能面对的社会层面与技术本质的偏离，形成新的探索与思考。

Artificial intelligence (AI) and quantum computing (QC) have emerged as two rapidly advancing computational paradigms, each facing distinct theoretical and practical challenges. In recent years, increasing attention has been directed toward their interaction, particularly in the context of hybrid quantum-classical systems suitable for noisy intermediate-scale quantum (NISQ) devices. This paper presents a structured analysis of the bidirectional relationship between AI and QC, examining how classical machine learning methods currently support quantum hardware operation, algorithm optimization, and error mitigation, while critically assessing the potential of quantum computing to enhance artificial intelligence in the longer term. Building on this analysis, we introduce a co-evolutionary co-design framework that characterizes feedback mechanisms across hardware, algorithmic, and application layers. The framework is intended as an analytical tool for understanding system-level dependencies rather than as a performance-optimizing prescription. To ground the discussion, we include a simulation-based case study in which a variational quantum circuit is optimized using a classical learning loop implemented with a hybrid quantum-classical workflow. The results demonstrate stable convergence behavior under idealized conditions, illustrating the feasibility of AI-assisted quantum optimization without claiming quantum advantage. By combining a critical literature review, a modest computational study, and a system-level design perspective, this work aims to provide a balanced reference for researchers investigating the near-term and long-term interactions between artificial intelligence and quantum computing.