Quantum computing (QC) is the emerging technology for solving complex problems through the laws of quantum mechanism and optimized algorithms. To examine the possibilities of active cooperation across both disciplines of computer science, this article explains the core components of quantum computing and conveys some of the important features linked by quantum computing and Artificial Intelligence (AI). To make major progress with artificial intelligence, quantum computing is probably necessary. Quantum computing’s training and processing power can be used to improve AI and machine learning (ML) algorithms at a reduced cost. AI can provide quantum computers with crucial error-correcting algorithms. Several AI algorithms have been successfully applied to quantum computers; they are constrained by the computational power of conventional computers despite developing useful applications. Artificial intelligence may benefit from quantum computing’s computational boost in order to handle more challenging issues. This case study explains the importance of quantum computing, the future of QC in AI, and the different applications of quantum computing, such as AI and ML, cybersecurity and cryptography, and weather forecasting.
Quantum computing (QC) is the emerging technology for solving complex problems through the laws of quantum mechanism and optimized algorithms. To examine the possibilities of active cooperation across both disciplines of computer science, this article explains the core components of quantum computing and conveys some of the important features linked by quantum computing and Artificial Intelligence (AI). To make major progress with artificial intelligence, quantum computing is probably necessary. Quantum computing’s training and processing power can be used to improve AI and machine learning (ML) algorithms at a reduced cost. AI can provide quantum computers with crucial error-correcting algorithms. Several AI algorithms have been successfully applied to quantum computers; they are constrained by the computational power of conventional computers despite developing useful applications. Artificial intelligence may benefit from quantum computing’s computational boost in order to handle more challenging issues. This case study explains the importance of quantum computing, the future of QC in AI, and the different applications of quantum computing, such as AI and ML, cybersecurity and cryptography, and weather forecasting.
Quantum artificial intelligence (QAI) is a topic of study that blends quantum computing and AI. In order to investigate the possibilities of fruitful cooperation across both disciplines of computer science (CS), the authors will attempt to convey some of the relevant properties of both artificial intelligence (AI) and quantum computing (QC) from an academic point of view. The key artificial intelligence applications that can be addressed more effectively by employing the current generation of quantum computers will be covered in this chapter, along with a review of the fundamental components of quantum computing. There are several similarities between quantum computing and artificial intelligence. Quantum computing can increase the training speed and processing capability of artificial intelligence and machine learning algorithms at a lower cost. This chapter discusses the fundamental ideas that enable scientists and engineers to combine AI with quantum computing.
The union of artificial intelligence (AI) and quantum computing (QC) is set to revolutionize computational power, ushering in a new era of technology. While machine learning (ML) revolutionized industries with the capability of making intelligent predictions based on data processing, it is circumscribed by the constraints of classical computer science. QC, which can process massive data concurrently with the aid of quantum bits (qubits), solves these limitations. Quantum machine learning (QML) involves QC but adds traditional ML algorithms to make the processing of complex data sets faster and efficient. Taking advantage of quantum properties like superposition and entanglement, QML can advance model training, speed up pattern recognition, and solve large-scale issues that classical ML cannot do or is impossible. This technology will see phenomenal advances in health care, cryptography, financial analysis, and drug development. The combination of AI and quantum computing will dramatically speed up the scope and scale of ML operations and open up new avenues to accomplish things in ways that were previously impossible. Although challenges exist in creating stable quantum systems and scalable algorithms, the pairing of AI, ML, and QML is a watershed moment in computational science.
The rapidly developing topic of quantum artificial intelligence (AI) combines machine learning with quantum computing to revolutionise data processing, boost efficiency, and address issues. However, quantum AI uses the ideas of superposition, entanglement, and quantum parallelism to execute complex computations much more quickly rather than relying on conventional binary computer systems. The promise of quantum AI in domains including materials science, healthcare, finance, and cryptography is examined critically in this study. Thus, by examining certain recent developments in quantum hardware, quantum algorithms, and hybrid quantum-classical models, this paper explores the revolutionary potential of quantum computing in artificial intelligence. Advances in quantum technology indicate a bright future for investigating the area of quantum AI in new computing challenges that conventional AI cannot address, notwithstanding the limits of noise, hardware, and error correction.
Quantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural Networks
This paper will review the basic building blocks of quantum computing and discuss the main applications in artificial intelligence that can be addressed more efficiently using the quantum computers of today. Artificial intelligence and quantum computing have many features in common. Quantum computing can provide artificial intelligence and machine learning algorithms with speed of training and computational power in less price. Artificial intelligence on the other hand can provide quantum computers with the necessary error correction algorithms. Some of the algorithms in AI that have been successfully implemented on a quantum computer, that we will present in this paper, are both unsupervised learning algorithms (clustering and Principal component analysis) and also supervised learning classification, such as support vector machines.
Quantum computing introduces revolutionary techniques for information processing by harnessing uniquely quantum phenomena. The emergence of quantum processors, algorithms, and software tools has brought this disruptive technology closer to practical applications. One of the most promising domains where quantum techniques can provide advantages is augmenting artificial intelligence. This chapter provides a comprehensive analysis of the fundamentals underpinning quantum computation, surveys the current hardware landscape, summarizes key quantum algorithms, and highlights promising applications in optimization, sampling, simulation, and machine learning. Engineering challenges in scaling up quantum systems are discussed along with progress in quantum error correction. Projections forecast steady advances towards quantum-enhanced AI based on a timeline of milestones. Realizing these synergies through collaborative interdisciplinary efforts promises to transform computing and catalyse innovations across industries to benefit science and society.