The convergence of quantum computing (QC) and artificial intelligence (AI) represents one of the most cutting-edge developments in the modern technological landscape. At the same time, multiple-valued logic (MVL), a generalization of classical binary logic, appears to be a promising tool for enhancing the expressiveness, scalability, and reasoning capabilities of intelligent systems. This paper explores the synthesis of quantum computing, quantum AI (QAI) and MVL and presents a systematic overview of their theoretical foundations, hardware architectures, and real-world applications. It serves as a survey report that rigorously studies the existing literature and technologies at the intersection of QC, QAI, and MVL. We examine how MVL is being implemented in both classical and quantum domains, particularly through multi-level memory systems, fuzzy logic controllers, and qudit-based quantum circuits. Furthermore, we analyze how MVL can support probabilistic inference, ambiguity modeling, and fault-tolerant computation in QAI frameworks. The paper also contrasts binary and multivalued logic systems across dimensions of complexity, physical realizability, and logical expressiveness, and highlights key industrial efforts by companies such as Intel, IBM, and Microsoft in advancing MVL and AI technologies. Finally, by synthesizing current literature and technology trends, we propose a conceptual framework that positions MVL as a foundational layer for scalable, intelligent quantum architectures, and analyze the potential of MVL-enhanced QAI systems to address challenges in areas such as biomedical diagnostics, decision support, and secure quantum communication.
The convergence of quantum computing (QC) and artificial intelligence (AI) represents one of the most cutting-edge developments in the modern technological landscape. At the same time, multiple-valued logic (MVL), a generalization of classical binary logic, appears to be a promising tool for enhancing the expressiveness, scalability, and reasoning capabilities of intelligent systems. This paper explores the synthesis of quantum computing, quantum AI (QAI) and MVL and presents a systematic overview of their theoretical foundations, hardware architectures, and real-world applications. It serves as a survey report that rigorously studies the existing literature and technologies at the intersection of QC, QAI, and MVL. We examine how MVL is being implemented in both classical and quantum domains, particularly through multi-level memory systems, fuzzy logic controllers, and qudit-based quantum circuits. Furthermore, we analyze how MVL can support probabilistic inference, ambiguity modeling, and fault-tolerant computation in QAI frameworks. The paper also contrasts binary and multivalued logic systems across dimensions of complexity, physical realizability, and logical expressiveness, and highlights key industrial efforts by companies such as Intel, IBM, and Microsoft in advancing MVL and AI technologies. Finally, by synthesizing current literature and technology trends, we propose a conceptual framework that positions MVL as a foundational layer for scalable, intelligent quantum architectures, and analyze the potential of MVL-enhanced QAI systems to address challenges in areas such as biomedical diagnostics, decision support, and secure quantum communication.
Quantum Artificial Intelligence (QAI) has emerged at the nexus of quantum computing and AI, promising to redefine computational frontiers. This survey critically synthesizes the state-of-the- art through 2024, elucidating the profound bidirectional synergy between these fields. We analyze how classical machine learning is accelerating quantum hardware control, circuit optimization, and error correction. Conversely, we assess the potential quantum advantage of algorithms, including variational and kernel-based methods, across domains such as drug discovery, financial modeling, and cybersecurity. Our analysis reveals a critical trade-of between the utility of near-term Noisy Intermediate-Scale Quantum (NISQ) devices and the long-term promise of fault-tolerant architectures. We identify fundamental obstacles to QAI’s advancement, including hardware decoherence, algorithmic barren plateaus, and data-encoding bottlenecks. While QAI’s potential is transformative, achieving practical quantum advantage requires a concerted effort to overcome these core challenges at the hardware-software interface. This work provides a roadmap for navigating the current landscape and prioritizing future research in this rapidly evolving discipline. • We introduce a bi-directional Quantum-AI framework, AI-for-QC and QC-for-AI, and provide a practical roadmap for integrating both paradigms across the full stack. • We quantify AI-enabled gains: calibration time drops from 8 hours to 2 hours, circuit depth falls by up to 42%, and ML decoders cut logical error rates by about half. • We synthesize comparative guidance on VQAs, quantum kernels, and hybrid/quantum Transformers, clarifying trainability limits and criteria for credible quantum advantage. • We deliver a clinically grounded playbook for radiology, covering small-data use cases, plug-in hybrid modules, OOD validation, fairness assessments, and TRIPOD/CONSORT-AI reporting.
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 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.
Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.
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.
The synergistic integration of quantum computational paradigms with artificial intelligence frameworks represents one of the most transformative technological frontiers of this decade. This comprehensive review provides a detailed technical examination of quantum artificial intelligence (QAI) from foundational principles to contemporary implementations. We systematically analyze the architectural frameworks of quantum machine learning systems, including quantum neural networks, variational quantum algorithms, and quantum-enhanced optimization methods. The paper presents a critical assessment of implementation challenges within the Noisy Intermediate-Scale Quantum (NISQ) computing paradigm, addressing decoherence effects, error mitigation methodologies, and the barren plateaus phenomenon. Through rigorous analysis of current literature and experimental implementations, we evaluate promising application domains including quantum chemistry, financial modeling, and materials science. The review concludes with a strategic roadmap for achieving practical quantum advantage in artificial intelligence applications, highlighting emerging research directions and hardware development trajectories.