The emergence of digital and cognitive Web 3.0 demands a move towards immersive applications that drive innovation across verticals like Industry 5.0, gaming, healthcare, and allied sectors. These sectors are led by key developments in Augmented Reality (AR) and Virtual Reality (VR), which offer end users a nuanced, haptic, and immersive Quality-of-Interaction (QoI). To manage end-user QoI, AR/VR applications rely on real-time, high-fidelity simulations, AI-driven control, and Next-Generation Wireless Communication Networks (NWGNs). Future applications face pressing needs for high computational capacity, resource optimization, parallel processing of vast amounts of data, and unbreakable security beyond classical cryptographic primitives. Integrating Quantum Computing (QC) into the AR/VR space offers transformative potential to overcome these challenges. Recent surveys have explored the potentials of AR/VR and QC in isolation, but the convergence of these fields remains largely unexplored. This survey aims to bridge this gap by exploring the potential of decoding QC into the AR/VR space. We systematically review existing surveys and present background discussions on QC, quantum AI, quantum resource optimization, and the role of NWGNs in QC-assisted AR/VR. Furthermore, we present a reference architecture for QC-assisted AR/VR, supported by a detailed solution taxonomy. Open issues and future research directions are outlined, and we discuss a novel case study of a QC-enabled Industrial Metaverse integrated with AR/VR. This survey serves as a pivotal resource for researchers and industry leaders aiming to pioneer the next generation of AR/VR experiences powered by QC systems, thereby shaping the future of immersive technologies.
The emergence of digital and cognitive Web 3.0 demands a move towards immersive applications that drive innovation across verticals like Industry 5.0, gaming, healthcare, and allied sectors. These sectors are led by key developments in Augmented Reality (AR) and Virtual Reality (VR), which offer end users a nuanced, haptic, and immersive Quality-of-Interaction (QoI). To manage end-user QoI, AR/VR applications rely on real-time, high-fidelity simulations, AI-driven control, and Next-Generation Wireless Communication Networks (NWGNs). Future applications face pressing needs for high computational capacity, resource optimization, parallel processing of vast amounts of data, and unbreakable security beyond classical cryptographic primitives. Integrating Quantum Computing (QC) into the AR/VR space offers transformative potential to overcome these challenges. Recent surveys have explored the potentials of AR/VR and QC in isolation, but the convergence of these fields remains largely unexplored. This survey aims to bridge this gap by exploring the potential of decoding QC into the AR/VR space. We systematically review existing surveys and present background discussions on QC, quantum AI, quantum resource optimization, and the role of NWGNs in QC-assisted AR/VR. Furthermore, we present a reference architecture for QC-assisted AR/VR, supported by a detailed solution taxonomy. Open issues and future research directions are outlined, and we discuss a novel case study of a QC-enabled Industrial Metaverse integrated with AR/VR. This survey serves as a pivotal resource for researchers and industry leaders aiming to pioneer the next generation of AR/VR experiences powered by QC systems, thereby shaping the future of immersive technologies.
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 metaverse intersects with the real world to establish a new era for wireless applications over next generation networks. Nevertheless, these applications are accompanied with a spate of challenges that can hinder their anticipated emergence. In particular, these applications are essentially capped with the profound communication and computing limits pertained by classical networks and end devices. Hence, to reap the full metaverse experience, a major leap that propels the metaverse apart from the classical paradigm of communication, computing, and artificial intelligence (AI) is demanded. In this paper, we shed light on the potential and recent advancements of quantum technologies that can drive the metaverse beyond the classical boundaries. In particular, with the substantial breakthrough witnessed in the versatile quantum streams, we leverage the quantum communication, computing, sensing, and AI tools that can pave the way for a groundbreaking version of metaverse applications to emerge. Up to this end, we present an overview of the metaverse in terms of its aspects, components, and persisting challenges. Then, we elaborate further on the applicable quantum tools (e.g., quantum federated learning, quantum key distribution, etc.) that can transform the different metaverse operational levels (e.g., digital twins, extended reality, etc). Finally, we present a set of open challenges and future research directions that arise along the lines of adopting quantum technologies for future metaverse applications.
The metaverse technology has gained quite a lot of hype lately on the reports that the big businesses want to create Metaverse settings for various applications including the game, assets, the virtual meetings and so on. Metaverse based apps are critical in guaranteeing fast and secure connection as well as integrated high-end technologies such as; virtual reality (VR), augmented reality (AR) and mixed reality (MR). Current hardware and software constraints in the Metaverse have to be addressed right away to be applied today. Problems with security, privacy, connection and complexity of computing are the major causes that slow down the formation of Metaverse. The exceptionally fast capabilities of quantum technology allow it to produce efficient scalable computing outputs at fast speeds. In the article we analyze quantum implementation methods that will work within the Metaverse environment thoroughly. Our study features a case analysis about implementing performance improvements in Metaverse apps through the combination of hybrid quantum kernel-based processing with optimized statistical methods feeding outputs into traditional supervised vector machines. The analysis presented here establishes fundamental research on Metaverse applications grounded in quantum mechanics principles for future investigations.
Semantic communications prioritize transmitting meaningful information over raw data in communication systems. However, these systems face significant optimization challenges, particularly concerning resource efficiency and fidelity due to the costly and delicate nature of photonic resources. Quantum computing offers promising solutions to these challenges through its unique capabilities, such as superposition and entanglement, within high-dimensional Hilbert spaces. This survey reviews the integration of quantum computing into semantic communications, tracing developments from foundational concepts to current advancements. It explores how quantum embeddings and machine learning techniques enhance semantic representation and transmission, enabling the encoding and processing of only relevant information. This approach addresses issues of polysemy and contextual variations in large datasets more effectively than classical methods. Key topics include the utilization of quantum probability and cognition in semantic analysis, optimization of quantum protocols for faster information retrieval, and the role of variational quantum circuits in improving computation latency, communication bandwidth, data privacy, and transmission delays. It also examines the implications for future communication systems like next-generation networks. It highlights the shift towards intelligent computing-intensive architectures and the support for advanced applications such as extended reality and holographic communications. Potential pathways are identified to achieve more efficient, secure, and intelligent data transmission, aiming to provide researchers and practitioners with a thorough understanding of this emerging field, outlining open research challenges and future directions to inspire further exploration and innovation.
In recent years, the interdisciplinary field of quantum computing has rapidly developed and garnered substantial interest from both academia and industry due to its ability to process information in fundamentally different ways, leading to hitherto unattainable computational capabilities. However, despite its potential, the full extent of quantum computing’s impact on healthcare remains largely unexplored. This survey paper presents the first systematic analysis of the various capabilities of quantum computing in enhancing healthcare systems, with a focus on its potential to revolutionize compute-intensive healthcare tasks such as drug discovery, personalized medicine, DNA sequencing, medical imaging, and operational optimization. Through a comprehensive analysis of existing literature, we have developed taxonomies across different dimensions, including background and enabling technologies, applications, requirements, architectures, security, open issues, and future research directions, providing a panoramic view of the quantum computing paradigm for healthcare. Our survey aims to aid both new and experienced researchers in quantum computing and healthcare by helping them understand the current research landscape, identifying potential opportunities and challenges, and making informed decisions when designing new architectures and applications for quantum computing in healthcare.
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.