The convergence between artificial intelligence (AI) and quantum computing has global economic and security implications, with applications ranging from advancing pharmaceuticals to breaking traditional encryption. In its national and local state plans, China's policymakers clearly seek global leadership in both of these domains, and encourage synergies between them. Traditional research institutions, namely universities and laboratories, are leading theoretical work and basic research and development for quantum computers to power AI applications, quantum machine learning (ML), and other foundational work. Additionally, China's premier cloud service providers are advancing the two technologies with quantum simulators and online platforms for ML and algorithm development. These companies are driving the commercialization of quantum technologies for industries like pharmaceuticals, materials science, and chemistry. China's investments and rapid advancements warrant analytic attention to avoid technological surprise and inform U.S. policymakers and leaders in these fields. To that end, this chapter identifies key entities pursuing research on the nexus between the two technologies, assesses technology paths and obstacles, and provides indicators for future analyses.
The convergence between artificial intelligence (AI) and quantum computing has global economic and security implications, with applications ranging from advancing pharmaceuticals to breaking traditional encryption. In its national and local state plans, China's policymakers clearly seek global leadership in both of these domains, and encourage synergies between them. Traditional research institutions, namely universities and laboratories, are leading theoretical work and basic research and development for quantum computers to power AI applications, quantum machine learning (ML), and other foundational work. Additionally, China's premier cloud service providers are advancing the two technologies with quantum simulators and online platforms for ML and algorithm development. These companies are driving the commercialization of quantum technologies for industries like pharmaceuticals, materials science, and chemistry. China's investments and rapid advancements warrant analytic attention to avoid technological surprise and inform U.S. policymakers and leaders in these fields. To that end, this chapter identifies key entities pursuing research on the nexus between the two technologies, assesses technology paths and obstacles, and provides indicators for future analyses.
Two powerful technologies, quantum computing and artificial intelligence (AI), can potentially disrupt sectors and solve some of society’s greatest problems in practically every industry. A study on how quantum computing and AI can work together. We seek to provide a comprehensive literature review encompassing key contributions and problems from both domains. The AI mission’s unique quantum computing strategy and how quantum algorithms can be used to slave for machine learning models and high-speed, sophisticated, accepted handling are explained. Quantum Computing has been shown to improve AI performance, efficiency, turnaround time, and problem-solving capacities. The research continues by considering what this means for quantum computing in AI, where transformational potential may exist, and where AI work currently limits quantum computing’s use. We also explore future research paths, emphasizing the need for interdisciplinary collaboration to properly utilize those technologies and overcome obstacles. This study should be a foundation for future research and discovery at the interface of quantum computing and AI, leading to ground-breaking applications and results.
The rapid convergence of quantum computing and artificial intelligence (AI) marks a transformative phase in computational sciences, promising groundbreaking solutions to problems beyond the reach of classical systems. This paper provides a comprehensive survey of the theoretical foundations, key advancements, and future challenges of integrating quantum computing into AI. The discussion begins with foundational quantum concepts, including the principles of superposition, entanglement, and interference, as well as mathematical frameworks such as Dirac notation and tensor product spaces. These concepts form the backbone of quantum algorithms and architectures designed to enhance AI capabilities. The paper delves into the state-of-the-art in quantum hardware, exploring the construction of quantum gates, circuits, and the innovations driving superconducting qubits, trapped ions, and photonic quantum systems. It reviews the contributions of leading industry and academic players in overcoming scalability and error correction challenges. Building on these hardware advancements, the survey examines prominent quantum algorithms, such as Shor’s algorithm for cryptography, Grover’s algorithm for search and optimization, and their potential to accelerate machine learning tasks. A significant portion of the survey focuses on quantum machine learning (QML), covering core architectures like Quantum Neural Networks (QNNs), Variational Quantum Circuits (VQCs), and Quantum Boltzmann Machines (QBMs). The paper also explores Quantum Support Vector Machines (QSVM) and Quantum Principal Component Analysis (QPCA), emphasizing their implementation, data encoding strategies, and optimization processes. Despite these advances, the paper identifies persistent challenges that hinder widespread adoption, including quantum decoherence, hardware scalability, and the need for modular architectures. Additionally, it considers the ethical and economic implications of quantum-AI integration, particularly as quantum technologies evolve from theoretical models to practical applications. The insights presented here aim to guide researchers, practitioners, and policymakers in shaping the future of this transformative field.
The growing integration of cloud computing and artificial intelligence (AI) has remarkably transformed modern computing, enabling organizations and researchers to process vast amounts of data and solve increasing complex problems. However, as data volumes and computational requirements continue to grow, traditional computing systems are increasingly challenged in meeting these demands. In this context, quantum computing has evolved as a revolutionary technology with the potential to perform certain computations far more efficiently than classical computers. When integrated with cloud platforms, quantum computing becomes accessible to a broader community through remote, on-demand services, giving rise to the concept of Quantum Cloud Computing. At the same time, Quantum Machine Learning (QML) is opening new possibilities by combining the power of quantum computing with advanced machine learning techniques. Together, these technologies are shaping a new paradigm known as Quantum Cloud Intelligence, where cloud infrastructure, quantum resources, and AI work in synergy to enhance computational capabilities and support intelligent decision-making. This chapter examines the foundations and evolution of Quantum Cloud Intelligence, highlighting its key technologies, architectures, and emerging applications. It explores how quantum-enabled cloud platforms can contribute to fields such as healthcare, finance, cybersecurity, scientific research, smart cities, and industrial automation. The chapter also discusses the opportunities offered by quantum-enhanced analytics, optimization, and predictive modelling, while addressing important challenges related to scalability, security, privacy, hardware constraints, and skill development. Looking ahead, the chapter considers future trends including hybrid quantum-classical systems, Quantum Computing as a Service (QCaaS), and the development of scalable quantum AI ecosystems. It argues that the integration of quantum computing, cloud technologies, and AI has the potential to redefine the future of intelligent computing and drive innovation across multiple disciplines. As these technologies continue to mature, Quantum Cloud Intelligence is expected to play a pivotal role in addressing complex global challenges and shaping the next generation of digital transformation.
This chapter explores the intersection of quantum computing, artificial intelligence (AI), and semiconductor technology, focusing specifically on the emerging field of quantum machine learning (QML). Quantum computing promises to revolutionize traditional machine learning algorithms by leveraging the principles of quantum mechanics to perform computations at exponentially faster speeds. This chapter will delve into the fundamentals of quantum computing and semiconductor technologies relevant to QML, highlighting the challenges and opportunities in scaling up integrated AI-quantum computing systems. It will discuss the convergence of AI and quantum computing, exploring the development of AI algorithms tailored for quantum information processing and the hardware implementations of quantum computing for AI acceleration. Case studies and industry applications will illustrate the potential of QML in cybersecurity, drug discovery, material science, and other domains, while addressing ethical and societal implications and future trends and challenges.
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.
The convergence of quantum computing and artificial intelligence (AI) is ushering in a transformative era of technological advancement. This comprehensive volume explores the historical evolution of quantum computing and AI, the mathematical principles underpinning quantum mechanics, advanced quantum algorithms, real-world applications, ethical concerns, and futuristic implications of AI-driven quantum systems. With breakthroughs in machine learning, cryptography, automation, and problem-solving, Quantum AI is poised to redefine computation, decision-making, and intelligence. This work serves as a definitive reference for researchers, engineers, and policymakers navigating the quantum-driven future.