This paper explores the emerging paradigm of Quantum-Enhanced Edge Intelligence (QEEI), which envisions the integration of quantum computing capabilities with distributed AI at the edge of networks. We review foundational concepts, highlight recent advances in quantum algorithms relevant to edge intelligence, and examine potential applications across domains such as autonomous systems and industrial IoT. The paper outlines a conceptual framework for QEEI, discusses current limitations in hardware and software integration, and offers a forward-looking perspective on research challenges and opportunities at the intersection of quantum computing and edge AI.
This paper explores the emerging paradigm of Quantum-Enhanced Edge Intelligence (QEEI), which envisions the integration of quantum computing capabilities with distributed AI at the edge of networks. We review foundational concepts, highlight recent advances in quantum algorithms relevant to edge intelligence, and examine potential applications across domains such as autonomous systems and industrial IoT. The paper outlines a conceptual framework for QEEI, discusses current limitations in hardware and software integration, and offers a forward-looking perspective on research challenges and opportunities at the intersection of quantum computing and edge AI.
Quantum computing and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making.The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities.This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems.The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities.At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks.The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum-AI synergy.By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.
The convergence of quantum computing and artificial intelligence represents a transformative technological paradigm with unprecedented potential for business intelligence applications. This comprehensive review critically examines the revolutionary capabilities of quantum computing-enhanced AI systems in addressing complex computational challenges across multiple business domains. Through systematic analysis of emerging research, implementation frameworks, and interdisciplinary case studies, we investigate how quantum computing's unique computational mechanisms can fundamentally reshape data analysis, strategic decision-making, and predictive modeling. Our comprehensive examination reveals that quantum AI systems demonstrate remarkable potential to reduce computational complexity by up to 90%, enhance predictive accuracy by 60-75%, and provide unprecedented insights across financial, logistical, and strategic business intelligence domains. The research synthesizes evidence from multiple technological domains, highlighting the transformative potential of quantum-enhanced AI in solving previously intractable computational problems. By exploring technological capabilities, implementation challenges, and future research directions, this review provides a critical framework for understanding the emerging intersection of quantum computing and artificial intelligence in advanced business intelligence applications.
The rapid convergence of quantum computing and artificial intelligence is reshaping computational paradigms across cryptography, optimization, sensing, and intelligent systems. This paper surveys and synthesizes recent developments at the intersection of quantum computing and AI, with particular attention to superconducting quantum hardware, quantum reinforcement learning, quantum key distribution optimization, and hybrid AI-driven post-quantum security frameworks. Drawing upon 2026 literature — including advances in high-speed predistortion architectures for flux-bias lines [1], enhanced quantum key distribution parameter optimization [2], quantum reinforcement learning for spectrum allocation in 6G networks [3], hybrid AI–post-quantum encryption for Internet of Medical Things (IoMT) systems [4], and measurement-consistent low-field MRI enhancement via k-space noise modeling [5] — we present a unified perspective on how quantum resources augment classical AI and how AI, in turn, accelerates the practical deployment of quantum technologies. We introduce a conceptual Quantum–AI Convergence Framework that maps hardware constraints, algorithmic opportunities, and security requirements. The analysis highlights persistent challenges in noise resilience, scalability, hybrid orchestration, and regulatory readiness, while outlining promising research directions for trustworthy quantum-enhanced intelligence.
Quantum - Enhanced Artificial Intelligence QAI merges the advanced capabilities of quantum computing with the sophisticated algorithms of artificial intelligence. This paper explores the current challenges in developing quantum algorithms and qubits, addressing issues of fidelity and scalability. It also examines the potential applications of QAI in various industries, including healthcare, cryptography, and optimization. This article also highlights the transformative potential of QAI, showcasing how the integration of quantum computing with AI can overcome current limitations in data processing and optimization, leading to groundbreaking advancements across multiple sectors.
Quantum -Enhanced Artificial Intelligence QAI merges the advanced capabilities of quantum computing with the sophisticated algorithms of artificial intelligence.This paper explores the current challenges in developing quantum algorithms and qubits, addressing issues of fidelity and scalability.It also examines the potential applications of QAI in various industries, including healthcare, cryptography, and optimization.This article also highlights the transformative potential of QAI, showcasing how the integration of quantum computing with AI can overcome current limitations in data processing and optimization, leading to groundbreaking advancements across multiple sectors.
The convergence of quantum computing and edge artificial intelligence (AI) represents a transformative advancement in real-time decision-making capabilities across diverse domains. This chapter explores the intricate relationship between these two cutting-edge technologies, emphasizing their synergistic potential to enhance urban management, healthcare, and industrial automation. By leveraging quantum computing's computational prowess alongside the low-latency processing capabilities of edge AI, organizations can address complex challenges and optimize operations in dynamic environments. The discussion delves into current quantum technologies relevant to edge AI, the architectural considerations for successful implementation, and the potential applications that can benefit from this integration. Additionally, significant technical challenges, such as hardware limitations, algorithm development, and security concerns, are examined, along with future directions to overcome these barriers. Ultimately, this chapter highlights the promising future of quantum edge AI as a pivotal solution for enhancing efficiency, sustainability, and responsiveness in the modern landscape.