As the development in quantum computation is on the rise, its potential application in various research areas has been investigated, including to artificial intelligence and machine learning (AI/ML). Computer vision, the fastest growing field in the AI/ML field, has also seen potential applications of quantum computing to solve computer vision tasks. In this paper, we discuss the current approach of utilizing quantum computing to computer vision. In particular, we firstly elaborate on classical approaches to solving several computer vision tasks, then briefly describe the background of different paradigms of quantum computers, i.e., the adiabatic quantum computer (quantum annealer), and the gate model, also known as the universal quantum computer. Subsequently, we summarize the problem formulation and quantum computing-based solutions to computer vision tasks that appeared in the major AI/ML conferences in the past years. Additionally, the strengths and drawbacks of each case are also discussed, providing beneficial insights into the current state of quantum computing usage for computer vision.
As the development in quantum computation is on the rise, its potential application in various research areas has been investigated, including to artificial intelligence and machine learning (AI/ML). Computer vision, the fastest growing field in the AI/ML field, has also seen potential applications of quantum computing to solve computer vision tasks. In this paper, we discuss the current approach of utilizing quantum computing to computer vision. In particular, we firstly elaborate on classical approaches to solving several computer vision tasks, then briefly describe the background of different paradigms of quantum computers, i.e., the adiabatic quantum computer (quantum annealer), and the gate model, also known as the universal quantum computer. Subsequently, we summarize the problem formulation and quantum computing-based solutions to computer vision tasks that appeared in the major AI/ML conferences in the past years. Additionally, the strengths and drawbacks of each case are also discussed, providing beneficial insights into the current state of quantum computing usage for computer vision.
With the exponential advancements in artificial intelligence (AI) and machine learning (ML), there’s a growing demand for more potent computing platforms to handle challenging tasks. Utilizing the principles of quantum physics, Quantum computers hold the potential to transform computing. As such, they are considered a promising technological advancement. The contribution of quantum computers to the development of AI and ML applications is examined in this conference paper. We go over the basic ideas of quantum computing, the latest developments, how they contrast with conventional computers and how they might affect AI and ML models and algorithms. We also draw attention to the difficulties and possibilities that lay ahead in using quantum computing into AI and ML processes.
Quantum computing has emerged as a transformative technology with the potential to revolutionize artificial intelligence (AI) and machine learning (ML). This paper explores the intersection of quantum computing and AI, focusing on how quantum principles can enhance computational capabilities and address challenges in traditional machine learning approaches. Key aspects discussed include quantum algorithms such as quantum support vector machines, quantum neural networks, and quantum variational algorithms, which leverage quantum superposition and entanglement to process vast amounts of data more efficiently than classical counterparts. These algorithms promise to accelerate tasks such as optimization, pattern recognition, and data classification, thereby advancing the capabilities of AI systems. Moreover, quantum computing offers potential breakthroughs in solving combinatorial optimization problems that are computationally intensive for classical computers. Quantum annealing and other quantum optimization techniques are explored for their application in AI, providing novel approaches to solving complex decision-making problems.
This paper aims to explore the application of quantum computing in the field of artificial intelligence (AI). The continuous development of AI technology has brought revolutionary changes to various industries, but traditional computers may encounter bottlenecks when facing complex problems. Quantum computing, as an emerging computing paradigm, has the potential to address complex issues. This paper first introduces the fundamentals of AI and quantum computing, then discusses the application cases of quantum computing in AI, including quantum neural networks, quantum optimization algorithms, and quantum reinforcement learning. It also analyzes the current technical challenges and solutions, and prospects the future development direction of quantum computing in the field of AI. Through research in this emerging field, we can better understand and utilize quantum computing technology to promote progress and innovation in the field of AI.
In recent years, quantum computing has emerged as a potentially gamechanging technology, with applications across various disciplines, including AI and machine learning.In recent years, the combination of quantum computing and neural networks has led to the development of quantum neural networks (QNNs).This paper explores the potential of QNNs and their applications in solving complex problems that are challenging for classical neural networks.This paper explores the fundamental principles of quantum computing, the architecture of QNNs, and their advantages over classical neural networks.Furthermore, this will highlight key research areas and challenges in the development and utilization of QNNs.Through an in-depth analysis, it demonstrates the QNNs hold significant promise for addressing complex computational problems and advancing the field of artificial intelligence.
Quantum computing, with its unique properties and potential to solve complex problems more efficiently than classical computers, has emerged as a promising field in the realm of computational science. The intersection of quantum computing and artificial intelligence (AI) presents a fertile ground for innovation, particularly in the development of quantum algorithms that can enhance machine learning models. This paper explores the theoretical foundations, current research, and potential applications of quantum computing in AI. We delve into the mathematical underpinnings of quantum algorithms, their integration with machine learning models, and the practical challenges and opportunities that lie ahead. Through a detailed analysis of existing research and the presentation of novel algorithms, this paper aims to provide a comprehensive overview of the current state and future prospects of quantum-enhanced AI
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