The development of quantum artificial intelligence (QAI) may lead a new computing revolution. This paper studies a model of quantum inference engine (QIE), which is a novel architecture designed to enhance quantum artificial intelligence by leveraging quantum principles. It discusses a role of the quantum superposition and entanglement in the transition from classical to quantum computational models, which surpasses the classical inference engines. The details of QIE’s structure is provided, from the quantum knowledge base to the inference mechanisms, demonstrating the capacity in the parallel processing and complex probabilistic reasoning. This research outlines the significant advancements in computational inference with the quantum technologies, especially in the era of the Noisy Intermediate-Scale Quantum (NISQ). The QIE shows its improved efficiency, scalability, and accuracy in handling intricate data and probabilistic models. The quantum inference engine will be useful for the research and applications in quantum artificial intelligence.
The development of quantum artificial intelligence (QAI) may lead a new computing revolution. This paper studies a model of quantum inference engine (QIE), which is a novel architecture designed to enhance quantum artificial intelligence by leveraging quantum principles. It discusses a role of the quantum superposition and entanglement in the transition from classical to quantum computational models, which surpasses the classical inference engines. The details of QIE’s structure is provided, from the quantum knowledge base to the inference mechanisms, demonstrating the capacity in the parallel processing and complex probabilistic reasoning. This research outlines the significant advancements in computational inference with the quantum technologies, especially in the era of the Noisy Intermediate-Scale Quantum (NISQ). The QIE shows its improved efficiency, scalability, and accuracy in handling intricate data and probabilistic models. The quantum inference engine will be useful for the research and applications in quantum artificial intelligence.
Quantum computing establishes a completely new method for performing calculations, which relies on three fundamental principles called superposition, entanglement, and probabilistic measurement. The application of artificial intelligence (AI) with quantum computing provides the ability to develop quantum AI (QAI) agents, which function as intelligent systems using quantum-based methods for representation, optimization, and probabilistic modeling. The research study analyzed the mathematical foundations of quantum computing, together with its key quantum algorithms used in AI and the architectural designs of hybrid quantum–classical agents, alongside quantum learning models and the complete implementation framework, which includes hardware systems, programming methods, and scalability challenges. The study examined different essential obstacles, which included decoherence, data encoding bottlenecks, gradient instability, benchmarking methods, and the comparison between theoretical and practical quantum advantage. Current quantum computing hardware exists within the noisy intermediate-scale quantum computing stage, which offers scientists various ways to investigate hybrid models and variational methods. Scalable and fault-tolerant quantum computing development will enable businesses to achieve groundbreaking results across various fields, including optimization, probabilistic reasoning, and intricate decision-making. The field of AI needs hardware engineering, algorithm advancement, error correction, and assessment methodology development before it can achieve practical quantum advantage. QAI represents an emerging interdisciplinary research frontier that integrates physics, computer science, mathematics, and ethics into a coherent research agenda.
Artificial Intelligence (AI) continues to drive innovation across industries, but faces mounting challenges in computational scalability, optimization, and interpretability as models grow increasingly complex. Quantum Artificial Intelligence (QAI) offers a promising path forward by leveraging quantum phenomena such as superposition and entanglement to augment learning processes beyond classical limits. This review synthesizes the recent progress in QAI, emphasizing a comparative perspective framed through its advantages and limitations. It examines how hybrid quantum-classical architectures and variational models seek to address constraints in the Noisy Intermediate-Scale Quantum (NISQ) era, and how applications in healthcare, finance, and cybersecurity illustrate both the potential and the barriers of the field. While several studies have broadly mapped the growth of QAI in algorithms and applications, this paper adopts a different perspective: emphasizing comparative analysis through advantages and limitations. The focus is on where QAI offers unique opportunities, where it encounters obstacles distinct from classical AI and quantum computing, and what research directions may transform these challenges into breakthroughs. By situating QAI within this comparative framework, the paper provides a realistic yet forward-looking assessment of its trajectory as a paradigm poised to reshape computational intelligence.
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.
Utilising quantum computing technology to enhance artificial intelligence systems is expected to improve training and inference times, increase robustness against noise and adversarial attacks, and reduce the number of parameters without compromising accuracy. However, moving beyond proof-of-concept or simulations to develop practical applications of these systems while ensuring high software quality faces significant challenges due to the limitations of quantum hardware and the underdeveloped knowledge base in software engineering for such systems. In this work, we have conducted a systematic mapping study to identify the challenges and solutions associated with the software architecture of quantum-enhanced artificial intelligence systems. The results of the systematic mapping study reveal several architectural patterns that describe how quantum components can be integrated into inference engines, as well as middleware patterns that facilitate communication between classical and quantum components. Each pattern realises a trade-off between various software quality attributes, such as efficiency, scalability, trainability, simplicity, portability, and deployability. The outcomes of this work have been compiled into a catalogue of architectural patterns. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board .
Contemporary predictive models face fundamental limitations when confronted with the intrinsic complexity of real-world systems. This paper introduces Quantum Artificial Intelligence (QAI) as a revolutionary paradigm poised to transcend these constraints. By harnessing the unique quantum mechanical properties of superposition and entanglement, QAI promises computational capabilities that dwarf those of classical methodologies, unlocking the potential to analyze colossal datasets and simulate intricate interactions with unprecedented fidelity. While traditional AI/ML techniques have reshaped numerous fields, the advent of quantum computing offers a synergistic enhancement, giving rise to new algorithms and approaches capable of addressing previously intractable problems. This paper explores the foundational principles underpinning QAI, examines the potential to reshape our approach to predictive modelling, and examines the conceptual landscape, current barriers, and prospective avenues for future research, offering a tantalizing glimpse into an era where predictive power surpasses conventional boundaries.