Objective: This research aims to evaluate and synthesise the development of artificial intelligence (AI) technologies integrated with quantum computing, especially regarding processing efficiency, hardware energy efficiency, and digital communication security. Research Design & Methods: This research utilizes the Systematic Literature Review (SLR) method of 13 scientific articles published in the ETRI Journal's 2024 special issue titled “Next-Gen AI and Quantum Technology.” The literature was selected based on inclusion criteria that included relevance to quantum AI, presence of experimental data, and contribution to computational efficiency. Findings: The study results show that approaches such as Quantum Reinforcement Learning and Quantum Kernel Classifiers can improve training efficiency and classification accuracy. Spiking Neural Networks technology reduced power consumption in AI-SoC and edge device designs. At the same time, the Quantum Key Distribution system demonstrated an error rate as low as 0.62% with WDM filter integration. The AONet video anomaly detection model achieves up to 97% AUC with the combination of a residual autoencoder and an attention module architecture. Implications & Recommendations: These findings indicate that quantum AI has great potential to overcome the limitations of classical computing in real-time applications and large-scale systems. However, challenges related to quantum noise, hardware stability, and integration with classical systems still need to be addressed. This research recommends strengthening hybrid infrastructure, developing interoperability standards, and utilizing multi-core architectures to support processing efficiency and data security. Contribution & Value Added: This study significantly contributes by systematically mapping existing methodologies and experiments in quantum AI, establishing a conceptual framework for future research avenues, and incorporating quantum AI technologies into industry, edge computing, and upcoming security systems
Objective: This research aims to evaluate and synthesise the development of artificial intelligence (AI) technologies integrated with quantum computing, especially regarding processing efficiency, hardware energy efficiency, and digital communication security. Research Design & Methods: This research utilizes the Systematic Literature Review (SLR) method of 13 scientific articles published in the ETRI Journal's 2024 special issue titled “Next-Gen AI and Quantum Technology.” The literature was selected based on inclusion criteria that included relevance to quantum AI, presence of experimental data, and contribution to computational efficiency. Findings: The study results show that approaches such as Quantum Reinforcement Learning and Quantum Kernel Classifiers can improve training efficiency and classification accuracy. Spiking Neural Networks technology reduced power consumption in AI-SoC and edge device designs. At the same time, the Quantum Key Distribution system demonstrated an error rate as low as 0.62% with WDM filter integration. The AONet video anomaly detection model achieves up to 97% AUC with the combination of a residual autoencoder and an attention module architecture. Implications & Recommendations: These findings indicate that quantum AI has great potential to overcome the limitations of classical computing in real-time applications and large-scale systems. However, challenges related to quantum noise, hardware stability, and integration with classical systems still need to be addressed. This research recommends strengthening hybrid infrastructure, developing interoperability standards, and utilizing multi-core architectures to support processing efficiency and data security. Contribution & Value Added: This study significantly contributes by systematically mapping existing methodologies and experiments in quantum AI, establishing a conceptual framework for future research avenues, and incorporating quantum AI technologies into industry, edge computing, and upcoming security systems
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 Artificial Intelligence (Quantum AI) embodies the integration of quantum computational paradigms with artificial intelligence frameworks to address the computational intractability and scalability constraints inherent in classical AI systems. By leveraging quantum mechanical properties such as superposition, entanglement, and quantum interference, Quantum AI enables enhanced representation learning, accelerated optimization, and improved probabilistic inference in high-dimensional problem spaces. In parallel, deep-tech innovations—including advanced deep learning architectures, neuromorphic computing, and edge-based intelligent processing—are redefining the architectural foundations of next- generation intelligent systems through energy-efficient, adaptive, and real-time computation. This paper presents a technically rigorous and comprehensive review of Quantum AI and deep-tech innovations, focusing on algorithmic formulations, architectural models, and system-level integration strategies. Core methodologies such as quantum machine learning algorithms, parameterized quantum circuits, variational quantum algorithms, and hybrid quantum–classical optimization frameworks are critically analysed with respect to computational complexity, repressibility, and feasibility on noisy intermediate-scale quantum (NISQ) hardware. Additionally, the role of neuromorphic computing in enabling spiking neural architectures and event-driven processing is examined as a complementary paradigm to quantum-enhanced learning models, particularly for low-power and latency-critical applications. The paper further investigates application- driven deployments of Quantum AI across domains including medical diagnostics, cryptographic security, autonomous decision-making, and smart cyber-physical systems, highlighting performance trade-offs and implementation constraints. Key challenges such as quantum noise, decoherence, data encoding bottlenecks, limited qubit connectivity, and ethical governance are systematically discussed. Finally, future research directions are identified, emphasizing fault-tolerant quantum learning architectures, scalable hybrid integration with deep-tech systems, and co-design methodologies for realizing practical next-generation intelligence platforms.
The field of Quantum Artificial Intelligence (Quantum AI) has emerged as a promising interdisciplinary research area where the computational power of quantum computing is merged with the learning and decision-making power of AI. The application of quantum principles such as superposition, entanglement and quantum interference to machine learning algorithms has created new possibilities for solving problems that may be difficult to solve by classical machines. In the last few years, advances in Noisy Intermediate-Scale Quantum (NISQ) devices have helped to speed advances in Quantum Machine Learning (QML) algorithms, Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Quantum Convolutional Neural Networks (QCNNs) and hybrid learning algorithms. This review provides a comprehensive overview of the research in the field of Quantum AI from 2020 to 2026, covering the key concepts, novel architectures, algorithmic advancements, and practical applications. A critical assessment of the application of Quantum AI in fields such as healthcare, finance, cybersecurity, scientific computing and optimization problems is provided, and the benefits of quantum-enhanced learning models are discussed. In addition, common difficulties such as quantum hardware limitations, sensitivity to noise, encoding of data, scalability constraints and a lack of speed in training variational quantum circuits are discussed. The review also covers new avenues of research, including trustworthy quantum machine learning, explainable Quantum AI, quantum federated learning, and fault-tolerant quantum computing. The results suggest while practical large-scale deployment is currently limited by existing hardware capabilities, hybrid quantum-classical systems have great potential to rapidly move intelligent data processing and develop next-generation AI systems.
Quantum computing is a new technological discovery that has the potential to transform industries based on high computational capabilities. This work explores how quantum computing will be integrated into AI applications and what impacts it will have on IT networks. A review of the recent literature shows that IT networks need to be upgraded to receive quantum-enhanced AI algorithms, as they require more computation power and faster real-time processing. It covers three major topics: the enabling of quantum AI applications, the role of QRC (quantum reservoir computing) in IT networking, and the challenges concerning the protocols of quantum communication, such as QKD (quantum key distribution). Network architectures of today’s state of the art will have to evolve toward enabling quantum-enabled AI, primarily regarding processing speed and interaction between the quantum and classical systems. This work, therefore, wishes to explain how such technological advances could influence AI applications and tune IT networks. We discuss the following questions: How can IT networks support the exploitation of quantum computing for AI applications? What effects do the dynamics and symmetries of quantum reservoir computing have on IT networks? Which IT networks can adapt to the challenges introduced by quantum computing technologies? The scope and depth of contributions reviewed in the articles together suggest huge potential for quantum computing in optimizing machine learning processes and IT networks with improved data handling and network management. At the same time, these ambitions are underlined by scaling concerns related to quantum hardware and qubit stabilization, and finally, the relative easiness with which quantum-classical computing is retrofitted into existing IT infrastructures. The findings suggest that hybrid quantum-classical systems will be essential in future IT infrastructure; efficiency and scalability will have to balance with security concerns in quantum computing environments.
Abstract This review aimed to explore the integration of Quantum Computing (QC) with Artificial Intelligence (AI) subsets such as Machine Learning (ML) and Deep Learning (DL), addressing the computational demands posed by the exponential growth of visual data. It identifies key challenges such as interdisciplinary complexity, lack of standard benchmarks, scalability, integration barriers, and the theoretical-practical gap in quantum applications. The review systematically examines existing literature on the application of quantum algorithms in areas including image processing, Natural Language Processing (NLP), Transfer Learning (TL), Federated Learning (FL), networking, cybersecurity and the finance sector. It highlights the usage of quantum principles like superposition and entanglement to accelerate computations, optimize models, and enhance data security in ML/DL frameworks. Findings indicate that integrating QC with ML/DL offers faster convergence, improved optimization, secure decentralized learning, and efficient handling of large-scale and complex data. Specific improvements are observed in TL and FL approaches, NLP accuracy, cryptographic robustness, and performance in medical diagnostics and autonomous systems. QC holds transformative potential in enhancing ML/DL capabilities across domains. Despite existing challenges such as error mitigation and integration complexity, its combination with classical learning methods opens new frontiers for research in AI-driven sectors. Future studies should focus on bridging theoretical and application-level gaps while creating standardized evaluation frameworks.
The integration of quantum computing and artificial intelligence (AI) constitutes a bidirectional synergy that is reshaping both disciplines. This review investigates the reciprocal relationship in which AI addresses foundational challenges in quantum computing, while quantum computing offers the potential to advance machine learning beyond classical constraints. Recent advancements exemplify this interaction: Google DeepMind’s AlphaQubit neural network decoder has achieved state-of-the-art quantum error correction, improving performance by 6% over tensor networks and 30% over correlated matching methods. Additionally, quantum neural networks have demonstrated exponential improvements in sample complexity for specific learning tasks. This review systematically examines the current landscape of quantum-AI integration across three primary dimensions. First, it addresses AI-enhanced quantum systems, such as transformer-based error correction, reinforcement learning for circuit optimization, and AI-driven hardware calibration. Second, it explores quantum-accelerated machine learning algorithms, including variational quantum neural networks, quantum generative adversarial networks, and quantum reinforcement learning. Third, it evaluates industry deployments in sectors such as life sciences, financial services, climate modeling, and pharmaceutical development. Notable examples include IonQ’s quantum chemistry simulations, which achieved 40% efficiency improvements in carbon capture material design; St. Jude’s identification of KRAS protein inhibitors using quantum machine learning with experimental validation; and JP Morgan Chase’s implementation of quantum portfolio optimization. The analysis indicates that the field has advanced from preliminary demonstrations to production-grade applications, especially in drug discovery and molecular simulation, where quantum computing offers measurable benefits. Nevertheless, several challenges remain, including barren plateaus in variational algorithms, scalability constraints in current noisy intermediate-scale quantum (NISQ) devices, requirements for real-time decoding speed, and the necessity for fault-tolerant quantum computing (FTQC) systems. IBM’s 2029 roadmap, which targets 200 logical qubits capable of executing 100 million gates, together with progress in high-rate quantum low-density parity-check codes, outlines a trajectory toward practical fault tolerance. This review makes two principal research contributions: Comprehensive Evaluation Framework for Quantum Advantage Assessment: We establish the first systematic, integrated methodology for determining quantum computing feasibility that combines problem characterization, resource estimation, quantum advantage assessment, and quantum algorithm paradigm selection. This framework consolidates criteria scattered across academic literature and industry practice into a unified decision-making tool applicable across chemistry, optimization, machine learning, and simulation domains. The framework addresses a critical industry need: enabling non-expert practitioners (chemists, financial analysts, materials scientists) to objectively assess quantum computing suitability without requiring deep quantum expertise. Novel Quantum Resource Optimization Algorithms: We present three concrete algorithmic contributions advancing quantum-AI integration: • Data Encoding Efficiency Algorithm that automatically selects optimal qubit encoding strategies (amplitude vs. angle encoding) minimizing total quantum resource consumption • Error Budget Optimization Algorithm that iteratively determines optimal quantum error correction code distance balancing logical error rate targets against physical qubit overhead • Real-time Hardware Specification Aggregation from multiple quantum cloud platforms (IBM Quantum, Amazon Braket, Google, IonQ) enabling dynamic feasibility assessment as hardware capabilities evolve Combined, these contributions establish quantum computing advantage assessment as a rigorous, data-driven discipline rather than ad-hoc expert judgment. The framework enables strategic quantum computing investment decisions, accelerates problem identification for early quantum utility, and provides a reference methodology for standardizing quantum advantage evaluation across academia and industry. This review synthesizes insights from recent academic literature, industry implementations, and expert perspectives to deliver a comprehensive assessment of quantum-AI synergies grounded in this evaluation framework. The mathematical foundations, ranging from quantum Fourier transforms to Gaussian processes, are examined. Best practices for researchers and practitioners are also outlined. The findings suggest that, although universal quantum advantage has not yet been realized, domain-specific quantum-AI applications in chemistry, optimization, and sensing are achieving practical utility in 2024-2025. This development marks a significant transition from theoretical potential to commercial realization, informed by rigorous evaluation methodologies.