Artificial Intelligence (AI) has achieved remarkable success across diverse application domains; however, its vulnerability to adversarial attacks poses significant challenges to reliability, security, and trustworthiness. Adversarial machine learning demonstrates that even highly accurate models can be manipulated through carefully crafted perturbations, raising serious concerns in safety-critical systems such as healthcare, finance, and autonomous technologies. In parallel, quantum computing has emerged as a transformative paradigm capable of addressing complex computational problems through principles such as superposition, entanglement, and quantum interference. The convergence of these fields has led to the emergence of Quantum Artificial Intelligence (QAI), which explores how quantum techniques can enhance learning efficiency, scalability, and robustness. This chapter provides a comprehensive overview of adversarial machine learning and existing defense strategies, followed by an accessible introduction to quantum computing and quantum machine learning models.
Artificial Intelligence (AI) has achieved remarkable success across diverse application domains; however, its vulnerability to adversarial attacks poses significant challenges to reliability, security, and trustworthiness. Adversarial machine learning demonstrates that even highly accurate models can be manipulated through carefully crafted perturbations, raising serious concerns in safety-critical systems such as healthcare, finance, and autonomous technologies. In parallel, quantum computing has emerged as a transformative paradigm capable of addressing complex computational problems through principles such as superposition, entanglement, and quantum interference. The convergence of these fields has led to the emergence of Quantum Artificial Intelligence (QAI), which explores how quantum techniques can enhance learning efficiency, scalability, and robustness. This chapter provides a comprehensive overview of adversarial machine learning and existing defense strategies, followed by an accessible introduction to quantum computing and quantum machine learning models.
Artificial Intelligence has achieved remarkable success across diverse application domains. However, its vulnerability to adversarial attacks poses significant challenges to reliability, security, and trustworthiness. Adversarial machine learning demonstrates that even highly accurate models can be manipulated through carefully crafted perturbations, raising serious concerns in safety critical systems such as healthcare, finance, and autonomous technologies. In parallel, quantum computing has emerged as a transformative paradigm capable of addressing complex computational problems through principles such as superposition, entanglement, and quantum interference. The convergence of these fields has led to the emergence of quantum artificial intelligence, which explores how quantum techniques can enhance learning efficiency, scalability, and robustness. This chapter provides a comprehensive overview of adversarial machine learning and existing defense strategies, followed by an accessible introduction to quantum computing and quantum machine learning models. It further presents conceptual frameworks for quantum-enhanced adversarial robustness, emphasizing quantum optimization, feature mapping, and hybrid quantum classical architectures. Practical applications, key challenges, and future research directions are also discussed to support the development of secure and trustworthy AI systems.
The convergence of quantum computing and artificial intelligence has introduced a new paradigm in computational science known as Quantum Artificial Intelligence (QAI). By leveraging quantum mechanical principles such as superposition, entanglement, and quantum parallelism, QAI aims to overcome the limitations of classical machine learning, particularly in handling high-dimensional data, complex optimization, and scalability issues. This paper presents a comprehensive review of foundational concepts in both classical machine learning and quantum computing, followed by an in-depth discussion of emerging quantum algorithms tailored for AI applications, such as quantum neural networks, quantum support vector machines, and variational quantum classifiers. We explore the practical implications of these approaches across key sectors, including healthcare, finance, cybersecurity, and logistics. Furthermore, we identify critical challenges related to hardware limitations, algorithmic stability, data encoding, and ethical considerations. Finally, we outline research directions necessary to advance the field, highlighting the transformative potential of QAI in shaping the next generation of intelligent technologies
The fast development of(AI) has radically changed the contemporary industries with its smart automation, predictive analytics, and decision-making based on data. However, the increasing complexity and scale of AI systems raise severe concerns of computational limits, the effectiveness of optimization, and huge amounts of data processing. The most important concepts that quantum computing as an emergent, disruptive paradigm is supposed to solve are superposition, entanglement and quantum parallelism. This chapter is a detailed talk on the convergence of quantum computing and AI which is commonly referred to as Quantum Artificial Intelligence and how it can enhance the effectiveness of machine learning, reduce the computation time, and address difficult optimization problems that are incapable of being resolved using classical systems. It also has an overview of how quantum-enhanced AI is used in addressing different fields, including healthcare, finance, cybersecurity, and logistics, and addresses such important concerns as hardware constraints, noise sensitivity, and scalability.
This chapter examines the convergence of quantum computing and artificial intelligence through the emerging field of Quantum Artificial Intelligence (QAI). The discussion explains how quantum principles such as superposition, entanglement, and quantum interference can enhance machine learning, generative AI, optimization, and intelligent systems beyond the limitations of classical computing. The chapter presents concepts of quantum computing, quantum machine learning, quantum neural networks, and hybrid quantum-classical architectures while also discussing applications in healthcare, cybersecurity, finance, manufacturing, and scientific research. In addition, the chapter addresses challenges related to scalability, explainability, cybersecurity, governance, ethics, and unequal access to quantum technologies. The chapter also introduces a conceptual QAI Ecosystem Model and outlines future research directions involving Quantum-enhanced Explainable AI, agentic AI, quantum internet systems, and quantum-safe cybersecurity.
With the rapid advancement of artificial intelligence (AI) and quantum computing, cybersecurity threats have evolved, giving rise to adversarial quantum attacks. These attacks exploit the vulnerabilities of AI models using quantum algorithms, posing a significant risk to data security, model robustness, and decision-making systems. This paper presents a comprehensive review of adversarial quantum attacks on AI, analyzing their mechanisms, potential impacts, and countermeasures. It explores how quantum computing can enhance adversarial attacks by accelerating the generation of adversarial examples, breaking cryptographic protections, and undermining AI model integrity. Additionally, the study examines different attack vectors, including quantum-enhanced adversarial perturbations, quantum machine learning (QML) vulnerabilities, and quantum decryption threats. The paper also discusses defensive strategies such as quantum-resistant AI models, quantum cryptographic defenses, and hybrid quantum-classical security frameworks that can mitigate these risks. By evaluating existing research and emerging trends, this review provides insights into the growing intersection of AI security and quantum computing. The findings emphasize the urgent need for robust quantum-aware AI security frameworks to safeguard AI-driven systems in the quantum era. Future research directions include developing quantum-adaptive AI models, post-quantum cryptographic techniques, and real-world applications of quantum-safe AI architectures. This review aims to contribute to the ongoing discourse on ensuring AI resilience against adversarial quantum threats, paving the way for a secure and quantum-resistant future.
Quantum-Enhanced Artificial Intelligence (QAI) integrates quantum computing with advanced AI techniques to transform computational intelligence in next-generation systems. By leveraging quantum properties such as superposition and entanglement, QAI enables faster processing, improved optimization, and enhanced learning capabilities beyond classical limits. This study highlights hybrid quantum-classical models and quantum machine learning approaches that improve pattern recognition and decision-making across domains such as healthcare, finance, and automation. It also addresses key challenges, including hardware limitations, noise, and scalability, along with the need for secure and ethical frameworks. Overall, QAI offers a powerful pathway toward faster, more adaptive, and scalable intelligent systems, driving innovation and shaping the future of AI-driven industries.