The convergence of traditional cyber-physical systems (CPS), quantum computing, and artificial intelligence (AI) gives rise to a novel system known as a quantum cyber-physical system (QCPS). This study aims to examine the integration of post-quantum models enabled by AI into quantum computing platforms and systems (QCPS). The merging of AI methodologies and the computational capabilities of quantum computers presents a novel approach to addressing intricate challenges in CPS. This context has several potential outcomes, including enhanced safety measures, improved resource allocation, and increased efficiency in quantum operations. Nevertheless, it is imperative to meticulously examine several challenges that arise in this context, including quantum decoherence, the interpretability of AI models, and the nascent stage of post-quantum algorithms. Overcoming these challenges will facilitate the advent of a novel era characterized by the integration of quantum-enabled systems, hence holding the capacity to revolutionize numerous domains within the economy and societal structure.
The convergence of traditional cyber-physical systems (CPS), quantum computing, and artificial intelligence (AI) gives rise to a novel system known as a quantum cyber-physical system (QCPS). This study aims to examine the integration of post-quantum models enabled by AI into quantum computing platforms and systems (QCPS). The merging of AI methodologies and the computational capabilities of quantum computers presents a novel approach to addressing intricate challenges in CPS. This context has several potential outcomes, including enhanced safety measures, improved resource allocation, and increased efficiency in quantum operations. Nevertheless, it is imperative to meticulously examine several challenges that arise in this context, including quantum decoherence, the interpretability of AI models, and the nascent stage of post-quantum algorithms. Overcoming these challenges will facilitate the advent of a novel era characterized by the integration of quantum-enabled systems, hence holding the capacity to revolutionize numerous domains within the economy and societal structure.
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.
The result of AI and quantum computing coming together is the novel idea known as Quantum Intelligence (QI). This work explores the possible advantages of combining artificial intelligence (AI) and quantum computing to address difficult problems that have proven to be beyond the capabilities of conventional computer methods. This study offers a thorough grasp of the unique properties of qubits, superposition, and entanglement while examining the fundamental concepts of quantum computing. The current state of artificial intelligence is also examined, with a focus on the difficulties traditional computers have in managing the increasing complexity of computing tasks. The paper's main focus is on Quantum Intelligence, providing an explanation of how quantum algorithms might enhance machine learning procedures and improve artificial intelligence algorithms. The paper also discusses Open Source Libraries, TensorFlow Quantum, and the necessity of quantum programming languages. This abstract discusses the advantages and challenges of integrating quantum computing into AI procedures, emphasizing the necessity for hybrid models and scalable algorithms. We explore the interdependent nature of quantum and classical machine learning models and show how quantum-enhanced algorithms may be able to outperform their classical counterparts in specific application domains. The conclusion of the abstract emphasizes how the development and use of open-source frameworks and tools will fuel the transformative potential of AI and quantum computing together.
Quantum computing introduces transformative opportunities but also unprecedented cybersecurity challenges. This study explores the integration of Artificial Intelligence (AI) with quantum computing to detect and mitigate emerging quantum-era cyber threats. By leveraging AI's pattern recognition capabilities and quantum computing's unparalleled processing power, this paper investigates hybrid models for real-time threat prevention. The study presents methodologies, results, and future directions for combining these technologies to enhance cybersecurity. Challenges such as hardware limitations and algorithmic inefficiencies are addressed, emphasizing AI's potential to create robust frameworks for quantum-secure systems
Quantum computing and artificial intelligence (AI) individually create massive waves in the oceans of technological innovation; however, when these two revolutionary powerhouses are combined, they have the potential to transform industries on a global scale. Quantum computing and AI are merging to redefine high-fidelity decision-making, combining raw processing power (“ultra-compute”) with adaptive intelligence (“ultra-learn”). This synergy unlocks unprecedented capabilities for solving complex problems beyond classical computing's reach. This capability to leverage quantum algorithms enables AI systems to handle easily larger and more sophisticated datasets, executing calculations at unprecedented speeds in the history of science. This leads to new configurations for machine learning, optimization, big data analysis, and more. Integrating these two modern fields must provide a means to circumvent many classical computing constraints, addressing the most challenging issues in medicine, finance, and cybersecurity. In particular, these foundational concepts are vital for any discussions regarding the convergence of quantum computing and AI or similar applications. As we embark on a new technological era, overcoming known computational barriers through the combination of quantum computing and AI models will be essential to achieving those technological milestones that will define our future. After all, innovation driven by AI applied to technology ensures progress.
This article examines the transformative potential of quantum computing in addressing the growing challenge of cyber threats. With traditional encryption methods becoming increasingly ineffective against sophisticated cyber attacks, quantum computing emerges as a promising solution, offering unparalleled computational capabilities for enhancing cyber security. This technology is poised to revolutionize how we protect sensitive data by developing quantum-resistant encryption algorithms and quantum-based machine learning modules to safeguard critical infrastructures. By exploring the intersection between quantum computing and cyber security, this article highlights the opportunities, challenges, and prospects of leveraging quantum advancements to strengthen our defenses against the evolving landscape of cyber threats
Rapid advances in quantum computing and artificial intelligence (AI) are reshaping cybersecurity by enhancing defensive capabilities while simultaneously introducing unprecedented threats to existing cryptographic systems. This paper examines the convergence of quantum computing and AI within the context of Pakistan’s emerging digital economy and its distinct geopolitical and environmental security challenges. As Pakistan continues to rely on classical cryptographic standards such as RSA and ECC, it faces growing risks from quantum-enabled attacks and increasingly sophisticated AI-driven cyber threats. Drawing on local literature, recent state-level cyber incidents in 2026, and emerging defense strategies, this study highlights the need for quantum-safe security frameworks, stronger data protection standards, and AI-driven cyber resilience in developing economies.