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.
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.
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.
Quantum computing represents a transformative shift from classical computational paradigms by utilizing quantum mechanical principles such as superposition, entanglement, and quantum interference.In the era of Artificial Intelligence (AI), the integration of quantum computing with information technology opens new possibilities for solving complex computational problems beyond the capabilities of traditional systems.This paper explores the conceptual foundations of quantum computing and its convergence with AI-driven information technologies.It examines how quantum algorithms enhance machine learning efficiency, optimize large-scale data processing, and strengthen cybersecurity through quantum cryptography.Furthermore, the study discusses current technological challenges including hardware instability, error correction, scalability issues, and the need for interdisciplinary expertise.Using analytical and secondary data sources, the paper evaluates emerging applications in healthcare, finance, communication networks, and scientific simulations.The research concludes that quantum-AI integration will redefine computational intelligence, enabling faster decision-making and innovative technological solutions.The study highlights future research directions and emphasizes the importance of national-level academic collaboration to accelerate quantum technology adoption.
This study explores the integration of quantum computing in the improvement of machine learning algorithms, highlighting its potential to overcome the computational limitations of classical methods in highly complex tasks. Through a systematic literature review with a qualitative approach, academic articles indexed between 2015 and 2023 were analyzed, identifying key advances in variational quantum algorithms, quantum support vector machines, and quantum neural networks. The findings reveal that, despite current technological constraints, these approaches show advantages in efficiency, representativeness and generalizability. Furthermore, the relevance of hybrid quantum-classical models as an intermediate solution is highlighted, by allowing a functional distribution between quantum and classical resources. The research concludes that this convergence represents a promising avenue for the development of advanced artificial intelligence, although challenges such as circuit optimization, noise mitigation and methodological standardization remain. The need to strengthen the technological and theoretical infrastructure to consolidate this line of computational innovation is emphasized.
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.
Quantum computing and artificial intelligence are two highly topical fields of research that can benefit from each other’s discoveries by opening a completely new scenario in computation, that of quantum artificial intelligence. Indeed, on the one hand, artificial intelligence algorithms can be made computationally more efficient due to the potential speedup enabled by quantum phenomena; on the other hand, the complex development of quantum computing technologies and methodologies can be properly supported by the use of classical artificial intelligence approaches. The “entanglement” of these two disciplines is opening up completely new directions in computer science research, and this survey aims to provide a systematic and taxonomic overview of the work that has already been done and that which will begin in the near future.
Quantum computing is an emerging technology (QC). Several groups and research institutions are working to make quantum computing applications a reality. Artificial Intelligence (AI) is another emerging field that is becoming more established over time. The main goal of this research is to figure out how the advancement of quantum computing technology will affect AI applications. Computational methods are used to explore the growing influence of quantum computing development for a specific AI application. This study also discusses about the impact and potential of Quantum Computing (QC) on Artificial Intelligence (AI). These can be accomplished by employing cutting-edge methods to deliver usable, economically viable, and scalable technology across a wide range of industries. On the other hand, the risk of committing errors in quantum algorithms, as well as the inability to function without being super-cooled, emerges as a major challenge. Macro-energy systems and the development of sustainable energy materials are just two areas of science and engineering that quantum computing has the potential to completely transform.