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Journal of Applied Optics

人工智能与量子计算：范式、挑战与展望

Jingzhao Zhou, 电子科技大学基础与前沿研究院，四川成都 611731, Zizhu Wang, Guangwei DENG

Published Jan 1, 2025• 0 citations

Abstract

人工智能（artificial intelligence，AI）和量子都是近年来既重要又引发了大量关注的领域。本综述旨在探讨人工智能与量子计算在“计算”范式中的双向作用：一方面分析人工智能在量子计算研究中的应用（AI for Quantum），另一方面评估量子计算对人工智能发展的潜在价值（Quantum for AI）。首先从函数拟合视角出发，系统阐释人工智能对科学问题的通用价值，再通过典型插值方法的比较实验、模型分析理论等，归纳出其在泛化性、可解释性和效率方面面临的核心难题。结果表明，人工智能在量子系统生命周期的各个阶段上均能起到助力效果，但受制于泛化与解释困难，其优势难以得到完全保障。同时，在量子赋能人工智能方面，本文通过复杂度理论和量子纠错成本估算，指出多数现有成果在工业应用层面缺乏可持续的优势，而“量子启发”的经典算法更具现实潜力。综上，我们认为人工智能与量子计算的结合虽已展示初步成果，但要实现长期突破仍需在理论基础与方法体系上进一步深化，尤其在可解释性和效率度量方面建立更坚实的框架。

DOI: 10.5768/jao202546.0612007

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人工智能（artificial intelligence，AI）和量子都是近年来既重要又引发了大量关注的领域。本综述旨在探讨人工智能与量子计算在“计算”范式中的双向作用：一方面分析人工智能在量子计算研究中的应用（AI for Quantum），另一方面评估量子计算对人工智能发展的潜在价值（Quantum for AI）。首先从函数拟合视角出发，系统阐释人工智能对科学问题的通用价值，再通过典型插值方法的比较实验、模型分析理论等，归纳出其在泛化性、可解释性和效率方面面临的核心难题。结果表明，人工智能在量子系统生命周期的各个阶段上均能起到助力效果，但受制于泛化与解释困难，其优势难以得到完全保障。同时，在量子赋能人工智能方面，本文通过复杂度理论和量子纠错成本估算，指出多数现有成果在工业应用层面缺乏可持续的优势，而“量子启发”的经典算法更具现实潜力。综上，我们认为人工智能与量子计算的结合虽已展示初步成果，但要实现长期突破仍需在理论基础与方法体系上进一步深化，尤其在可解释性和效率度量方面建立更坚实的框架。

The widespread use of artificial intelligence has positioned it at the pinnacle of the technical hierarchy. Nevertheless, artificial intelligence (AI) outperforms traditional computers in certain aspects. The potential for quantum processing to greatly augment the efficacy of machine learning and artificial intelligence has emerged as a result of technological progress. Subsequent investigations will be undertaken to examine the impact of quantum processing on artificial intelligence (AI) and its implications for many sectors and economic systems. This research investigates the impact of quantum computing on artificial intelligence (AI), the distinctions between quantum and conventional computing, and the many applications of quantum computing in the field of AI.

本节批判性地分析了人们在讨论大脑决策时引入“多种可能性”与“量子随机性”的常见做法，指出这种做法在物理基础上缺乏一致性。通过将人脑与建筑物、种子、芯片等宏观物理系统进行类比，阐明即使存在量子层面的微观不确定性，其在宏观系统中的作用仍然微弱到不足以决定宏观行为的结果。因此，认为大脑在某一时间点可以“选择”多种行为，仅仅是因为观察者对过程细节的无知，而非这些行为在物理上真正存在多种可能性。该节强调人脑的决策过程应被视作经典物理系统的演化，类似于计算机电路，而非量子系统中的随机塌缩。

Quantum Artificial Intelligence (QAI) stands for the combination of the exercise of ‘quantum computing’ and ‘artificial intelligence’ that drives breakthrough transformation across various industries. In this paper, it is discussed on how QAI is bringing change across industries including pharmaceuticals, finance, weather prediction, supply chain, security, smart cities, agriculture, telecommunication, and education. With quantum mechanics conceptions, QAI enhance computation ability to address more data, and enhance the algorithms. Quantum technologies have potential for attachment to AI systems and make the previously unachievable innovations with the help of overgrowing the classical models. As a conclusion, this paper also discusses its result on the methodological difficulties and ethical issues of QAI, the need to build strategic partnerships and the need for well-regulated legislation. The results extend our understanding of QAI to apprehend the future of technology and its application for solving some of the greatest problems that the world faces today.

Quantum拓扑优化是量子计算领域一个新兴且具有挑战性的研究方向,旨在优化量子系统的参数以提高计算精度和效率。传统方法通常采用固定拓扑,难以适应量子系统的动态特性。本文提出了一种基于动态自适应的量子计算的量子拓扑优化框架,该框架能够根据输入数据的动态特性,自动调整量子拓扑结构的参数,从而实现更有效的量子计算。该框架的核心机制是通过动态调整参数,优化量子系统的量子态,从而提升计算能力。我们提出了一种新的优化算法,该算法能够有效应对复杂量子系统,并展示了在特定应用场景中的优越性能。

Quantum拓扑,一种量子力学中存在的奇特结构,为量子计算提供了独特的优势。本研究提出了一种基于量子拓扑的自适应量子计算量子路径优化方法,旨在利用拓扑结构优化量子路径,从而提高计算效率。该方法借鉴了量子拓扑的特性,通过构建拓扑结构来指导量子路径的优化,从而实现更高效的量子计算。该方法具有自动学习能力,并能够针对不同问题优化量子路径,为量子计算的发展提供新的思路。

Quantum artificial intelligence (QAI) is a topic of study that blends quantum computing and AI. In order to investigate the possibilities of fruitful cooperation across both disciplines of computer science (CS), the authors will attempt to convey some of the relevant properties of both artificial intelligence (AI) and quantum computing (QC) from an academic point of view. The key artificial intelligence applications that can be addressed more effectively by employing the current generation of quantum computers will be covered in this chapter, along with a review of the fundamental components of quantum computing. There are several similarities between quantum computing and artificial intelligence. Quantum computing can increase the training speed and processing capability of artificial intelligence and machine learning algorithms at a lower cost. This chapter discusses the fundamental ideas that enable scientists and engineers to combine AI with quantum computing.