Introduction
The rapid evolution of healthcare systems in the digital era has created an urgent demand for intelligent, secure, and efficient solutions capable of addressing increasingly complex medical challenges, as traditional computational approaches are reaching their limits in handling the massive scale, heterogeneity, and real-time requirements of modern healthcare data; in this context, the convergence of quantum computing and artificial intelligence (AI) emerges as a transformative paradigm with unprecedented potential to redefine smart healthcare systems. Quantum computing, grounded in principles such as superposition and entanglement, enables fundamentally new ways of processing information and offers the ability to solve highly complex optimization, simulation, and data analysis problems far beyond the capabilities of classical systems, making it particularly relevant for domains such as drug discovery, genomics, medical imaging, and personalized medicine. At the same time, AI has already revolutionized healthcare through intelligent diagnostics, predictive analytics, and decision support systems, with machine learning and deep learning models increasingly integrated into clinical workflows to enable early disease detection, automated image analysis, and improved patient outcomes, despite ongoing challenges related to scalability, data privacy, interpretability, and computational efficiency. The integration of quantum computing with AI, often referred to as quantum intelligence or quantum-enhanced machine learning, represents a promising frontier, where the synergy between quantum computational power and intelligent algorithms can accelerate learning processes, enhance pattern recognition, and address previously intractable problems, thereby enabling the development of faster, more accurate, and more secure healthcare solutions. In parallel, the emergence of smart healthcare systems driven by the Internet of Medical Things (IoMT), big data analytics, and cloud and edge computing is transforming healthcare delivery through continuous monitoring, real-time data collection, and remote patient care, while simultaneously introducing challenges related to security, privacy, interoperability, and scalability, areas where quantum technologies can contribute significantly, particularly through advanced cryptographic and secure communication mechanisms. This book, Quantum Computing and Intelligence for Smart Healthcare Systems, provides a comprehensive exploration of these intersecting domains by presenting cutting-edge research, innovative methodologies, and practical applications that demonstrate how quantum computing and intelligent systems can enhance healthcare delivery, covering topics such as quantum machine learning, AI-driven healthcare analytics, secure and privacy-preserving frameworks, blockchain integration, and real-world smart healthcare implementations; it also addresses key challenges and future directions, including practical deployment considerations, ethical implications, and technological readiness, ultimately serving as a valuable resource for researchers, practitioners, and policymakers and underscoring that the integration of quantum computing and intelligence into smart healthcare systems represents not merely an incremental improvement but a fundamental paradigm shift with the potential to significantly enhance the quality, accessibility, and sustainability of healthcare worldwide.