Vanessa García Pineda , Alejandro Valencia-Arias , Francisco Eugenio López Giraldo , Edison Andrés Zapata-Ochoa
{"title":"Integrating artificial intelligence and quantum computing: A systematic literature review of features and applications","authors":"Vanessa García Pineda , Alejandro Valencia-Arias , Francisco Eugenio López Giraldo , Edison Andrés Zapata-Ochoa","doi":"10.1016/j.ijcce.2025.08.002","DOIUrl":null,"url":null,"abstract":"<div><div>Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 26-39"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Cognitive Computing in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266630742500035X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.
量子计算(QC)和人工智能(AI)已经成为工业6.0发展的关键技术,推动了自动化和高级分析以及流程优化的进步。它们的整合有可能通过量子比特、叠加和量子纠缠实现更快、更高效的计算,从而彻底改变数据科学、医疗保健、金融和网络安全等领域。然而,缺乏关于特定QC方法和人工智能应用的结构化知识阻碍了其最佳实施和发展。因此,本研究旨在确定与QC-AI集成相关的应用和变量。为此,根据PRISMA 2020方法,利用Scopus和Web of Science数据库的研究进行了系统的文献综述。这使得分析技术融合的趋势、限制和机会成为可能。本研究旨在通过确定定义该领域现状的关键技术特征、集成要求和部门应用,系统地检查量子计算和人工智能的交叉点。该综述通过绘制现有研究,突出方法方法,并揭示可能指导混合量子人工智能系统有针对性进展的差距来做出贡献。由此产生的见解有可能加速医疗、金融、能源和网络安全等高影响力领域的创新。研究结果表明,应用于人工智能的QC的主要进展集中在量子优化、量子机器学习(QML)和后量子密码学上。值得注意的是,能源、医疗保健和金融等行业在采用这些技术方面取得了重大进展。例如,在医疗保健领域,QML已被应用于模拟分子相互作用,以加速药物发现;在金融领域,QML增强了市场行为的预测模型。该研究的结论是,尽管QC显示出增强人工智能的巨大潜力,但其更广泛的采用仍然受到对NISQ硬件的依赖、有效纠错的需求以及混合量子经典算法有限的可扩展性的限制。解决这些挑战对于将QML打造成技术创新和数字化转型的基石至关重要。此外,本文还介绍了一个综合框架,该框架对关键的人工智能QC融合维度进行了分类,并根据技术要求和算法能力对应用领域进行了分类。这些贡献旨在指导未来的实验验证和混合模型的开发。