Technological and scientific advancements in recent years have enabled us to manipulate technologies that were once considered unimaginable. Among these, Artificial Intelligence (AI) and quantum physics have emerged as pivotal forces, significantly enhancing our capacity to achieve goals and solve complex problems. Given their growing relevance, a comprehensive review of the scientific output underpinning this new era is both timely and necessary. Such a review allows us to trace the increasing scholarly interest in these domains and to identify emerging trends in the synergy between them. Accordingly, the objective of this article is to present a scientometric analysis of the academic literature related to both AI and quantum science. To achieve this, we examined peer-reviewed articles from prestigious databases such as Scopus and Web of Science. The study spans the period from 2004 to 2024, analyzing key metrics such as publication growth, influential authors, leading institutions, and the geographic distribution of research. The results reveal an exponential growth trajectory, with a marked increase in interdisciplinary publications over the last decade. This analysis provides a structured overview of the intersection between quantum computing and AI, offering a solid foundation for researchers and practitioners to identify priority areas for future innovation.
Technological and scientific advancements in recent years have enabled us to manipulate technologies that were once considered unimaginable. Among these, Artificial Intelligence (AI) and quantum physics have emerged as pivotal forces, significantly enhancing our capacity to achieve goals and solve complex problems. Given their growing relevance, a comprehensive review of the scientific output underpinning this new era is both timely and necessary. Such a review allows us to trace the increasing scholarly interest in these domains and to identify emerging trends in the synergy between them. Accordingly, the objective of this article is to present a scientometric analysis of the academic literature related to both AI and quantum science. To achieve this, we examined peer-reviewed articles from prestigious databases such as Scopus and Web of Science. The study spans the period from 2004 to 2024, analyzing key metrics such as publication growth, influential authors, leading institutions, and the geographic distribution of research. The results reveal an exponential growth trajectory, with a marked increase in interdisciplinary publications over the last decade. This analysis provides a structured overview of the intersection between quantum computing and AI, offering a solid foundation for researchers and practitioners to identify priority areas for future innovation.
Technological and scientific advancements in recent years have enabled us to manipulate technologies that were once considered unimaginable. Among these, Artificial Intelligence (AI) and quantum physics have emerged as pivotal forces, significantly enhancing our capacity to achieve goals and solve complex problems. Given their growing relevance, a comprehensive review of the scientific output underpinning this new era is both timely and necessary. Such a review allows us to trace the increasing scholarly interest in these domains and to identify emerging trends in the synergy between them. Accordingly, the objective of this article is to present a scientometric analysis of the academic literature related to both AI and quantum science. To achieve this, we examined peerreviewed articles indexed in the Web of Science (WoS) and Scopus databases, focusing on key indicators such as publication volume, citation patterns, and collaboration networks among countries, journals, and authors. The findings reveal a notable increase in interest toward the integration of AI and quantum technologies in recent years, driven by their combined potential to deliver enhanced performance across a wide range of disciplines.
This article focuses on the transformation of artificial intelligence (AI) from its beginnings in classical paradigms to its contemporary development in the context of quantum computing. As artificial intelligence has evolved, it has proven to be an essential tool in various sectors, from health going through education to finance. The main purpose of this article is to explore the evolution of artificial intelligence from its classical foundations to emerging innovations in quantum computing. This is a qualitative research, framed in a methodology of a type of documentary study, using the technique of documentary review, which consisted of a critical and systematic analysis of previously prepared documents, with the purpose of extracting pertinent and relevant information, making it possible to detail the analyzed phenomenon by characterizing this technology. One of the main findings emanating from this research reveals that quantum algorithms can perform computational tasks that, in their classical version, would consume an exorbitant amount of time. This way, it is concluded that the integration of quantum computing into artificial intelligence is not only viable, but could also mark a before and after in the field. However, the need to continue researching the practical implementation and scalability of these technologies is emphasized so that their potential is fully realized, opening a new horizon for the development of innovative solutions in the different disciplines of science, technology and scientific research.
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.
This article discusses the development trends of artificial intelligence and quantum physics, as well as their role in modern science and technology. The fundamental concepts of quantum mechanics, the operating principles of quantum computing systems, and their advantages in data processing are analyzed. In addition, the study examines the new opportunities emerging from the integration of artificial intelligence algorithms with quantum technologies, their efficiency in solving complex computational problems, and promising future directions. The findings indicate that the integration of artificial intelligence and quantum technologies serves as an important factor in the advancement of scientific research and various applied fields.
Quantum computing and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making.The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities.This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems.The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities.At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks.The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum-AI synergy.By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.
Quantum computing (QC) has the potential to be the next abstruse technology, with a wide range of possible applications and ramifications for organizations and markets. QC provides an exponential speedup by employing quantum mechanics principles, including superposition and entanglement. The potential advantages offered by the revolutionary paradigm have propelled scientific productions. Therefore, a highly pertinent investigation is required to elucidate the evolution of path-breaking trajectories for scientific advances. This study confronts the idea by presenting a scientometric analysis for the recent decade of literature collected from the Web of Science database in the computer science discipline. The scientometric implications of the article identify the significant research domains and provide an intensive insight into the publication patterns, country collaboration, geographical analysis, citation patterns, eminent journals, and research frontiers of each domain of QC. The scholarly literature analysis identifies key challenges in the QC knowledge domain. Overall, the inference reveals an evolutionary pathway for future research directives and collaboration in the domains of QC research. The research findings provide innovative significance to information scientists by presenting a comprehensive overview of QC research to help them find relevant applications, research topics, and key challenges.