Abstract Quantum imaging exemplifies the fascinating and counter‐intuitive nature of the quantum world, where non‐local correlations are exploited for imaging of objects by remote and non‐interacting photons. The field has exploded of late, driven by advances in our fundamental understanding of these processes, but also by advances in technology, for instance, efficient single photon detectors and cameras. Accelerating the progress is the nascent intersection of quantum imaging with artificial intelligence and machine learning, promising enhanced speed and quality of quantum images. This review provides a comprehensive overview of the rapidly evolving field of quantum imaging with a specific focus on the intersection of quantum ghost imaging with artificial intelligence and machine learning techniques. The seminal advances made to date and the open challenges are highlighted, and the likely trajectory for the future is outlined.
Abstract Quantum imaging exemplifies the fascinating and counter‐intuitive nature of the quantum world, where non‐local correlations are exploited for imaging of objects by remote and non‐interacting photons. The field has exploded of late, driven by advances in our fundamental understanding of these processes, but also by advances in technology, for instance, efficient single photon detectors and cameras. Accelerating the progress is the nascent intersection of quantum imaging with artificial intelligence and machine learning, promising enhanced speed and quality of quantum images. This review provides a comprehensive overview of the rapidly evolving field of quantum imaging with a specific focus on the intersection of quantum ghost imaging with artificial intelligence and machine learning techniques. The seminal advances made to date and the open challenges are highlighted, and the likely trajectory for the future is outlined.
Quantum imaging is an emerging field based on quantum mechanics and imaging technology, capable of unique capabilities in resolution, sensitivity, and information processing. An extensive review of the concepts, methods, and uses of quantum imaging is given in this chapter. The main ideas of quantum mechanics that are relevant to imaging, including superposition, entanglement, and quantum measurement, are introduced in this chapter. Following this, the various quantum imaging techniques, including quantum entanglement imaging, ghost imaging, and quantum lithography, illuminating their operational mechanisms and advantages over classical methods, are explored. The role of advanced photonic systems, single-photon detectors, and quantum dots in enhancing image quality are discussed. Additionally, the incorporation of quantum computing and machine learning with quantum imaging is examined, highlighting how these interdisciplinary approaches are pushing the boundaries of what can be visualized and analysed. Quantum imaging applications are varied, groundbreaking, and innovative, ranging from medical diagnostics and biological imaging to quantum communication and fundamental physics research. By harnessing the exclusive properties of quantum states, these applications achieve higher precision and new capabilities that classical imaging techniques cannot match. The challenges and future directions of quantum imaging, considering both the technical hurdles and the potential for groundbreaking discoveries, are discussed as well. In a nutshell, the chapter aims to offer the readers an in-depth insight into quantum imaging, its current state, and its future prospects by providing a holistic view of how quantum imaging is composed to revolutionize various scientific and technological fields.
Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.
Quantum computing, with its unique properties and potential to solve complex problems more efficiently than classical computers, has emerged as a promising field in the realm of computational science. The intersection of quantum computing and artificial intelligence (AI) presents a fertile ground for innovation, particularly in the development of quantum algorithms that can enhance machine learning models. This paper explores the theoretical foundations, current research, and potential applications of quantum computing in AI. We delve into the mathematical underpinnings of quantum algorithms, their integration with machine learning models, and the practical challenges and opportunities that lie ahead. Through a detailed analysis of existing research and the presentation of novel algorithms, this paper aims to provide a comprehensive overview of the current state and future prospects of quantum-enhanced AI
Quantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural Networks