Quantum Artificial Intelligence: Convergence of Quantum Computing and Machine Intelligence

H. Meenal, Md. Shoeb Atthar, Harika Koormala +1 more

Dr. Laura Green

The convergence of Artificial Intelligence (AI) and Quantum Computing has the potential to revolutionize multiple industries by solving complex problems that are beyond the reach of classical computing. This article explores the intersection of AI and quantum computing, focusing on how quantum algorithms and quantum machine learning can enhance AI capabilities, speed up computations, and enable new types of AI models. The article discusses the benefits of combining AI and quantum computing, the challenges of this convergence, and future directions for research and applications.

Shenson Joseph, Herat Joshi, Md. Mehedi Hassan +3 more

Achraf Abdelghafour Zemate, Ismail El Gaabouri, Moulay Brahim Sedra

Abstract Artificial intelligence (AI) and quantum computing represent a new frontier in computing, offering the promise of solving problems previously considered unsolvable. This article aims to provide an in-depth analysis of the current state of research at the intersection of quantum computing and artificial intelligence. We will explore the basic concepts, major applications, and challenges of quantum machine learning (QML). We will start by offering a definition of the motivation for the convergence of quantum computing and artificial intelligence. We will then proceed to offer a description of the basic algorithmic frameworks for quantum machine learning, such as quantum differential algorithms (VQA) for near-term quantum (NISQ) devices and the prospects for error-tolerant quantum algorithms such as the Harrow-Hassidim-Lloyd algorithm. We will then proceed to offer a methodical analysis of the current applications of quantum machine learning in finance, cybersecurity, materials science, among other fields. We will then proceed to discuss the basic challenges facing the implementation of quantum machine learning, while offering a glimpse of the future of quantum-enhanced artificial intelligence.

Gustina Alfa Trisnapradika, Muhamad Akrom

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