Many countries have invested a large amount of scientific research funds into supporting quantum information technology and proposed many policies to support the three main quantum technology fields: quantum sensing, quantum communication and quantum computing. Many science and technology giants have also provided quantum cloud computing experimental platforms that can provide quantum access online. With the investment of various technologies and resources, quantum information technology has developed rapidly. The combination of quantum computing and neural networks is a research branch that has attracted much attention in the field of quantum computing in recent years. It mainly involves the study of how to exploit the computational acceleration advantage of quantum computing to deal with various problems solved by neural networks in traditional artificial intelligence. In this paper, the recent development of the quantum computer is widely investigated, and a quantum neural network is briefly analyzed. Finally, the performance of the quantum neural network and quantum optimization is discussed, and future research directions are proposed.
Many countries have invested a large amount of scientific research funds into supporting quantum information technology and proposed many policies to support the three main quantum technology fields: quantum sensing, quantum communication and quantum computing. Many science and technology giants have also provided quantum cloud computing experimental platforms that can provide quantum access online. With the investment of various technologies and resources, quantum information technology has developed rapidly. The combination of quantum computing and neural networks is a research branch that has attracted much attention in the field of quantum computing in recent years. It mainly involves the study of how to exploit the computational acceleration advantage of quantum computing to deal with various problems solved by neural networks in traditional artificial intelligence. In this paper, the recent development of the quantum computer is widely investigated, and a quantum neural network is briefly analyzed. Finally, the performance of the quantum neural network and quantum optimization is discussed, and future research directions are proposed.
In recent years, artificial neural networks have been widely used in various fields and have shown strong computational intelligence. At the same time, quantum computing hardware has been developed rapidly. In particular, near term quantum processors have relatively stable computing capabilities and robustness against decoherence. With the aid of this powerful quantum technique, nowadays many commercial cloud quantum computing companies can provide online quantum computing experimental platforms for scholars around the world. Various technical developments have promoted the emergence of many new research ideas and experimental progress in the cross-field of quantum neural networks. In this paper, the research ideas of early quantum neural networks are summarized first. The data encoding, algorithm flows, and quantum circuit design of quantum neural networks based on recent quantum processors are then reviewed and analyzed. Finally, several key scientific problems in the field of quantum neural networks are summarized, and prospective future research directions are presented.
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.
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, there has been remarkable progress in improving the availability of resources and refining algorithms for quantum computing. Since the late 1980s, the scientific community has been fascinated by the idea of harnessing quantum phenomena to tackle computational problems. This article provides a comprehensive exploration of the foundational theories and practical applications of quantum neural networks (QNNs), highlighting their potential to transform machine learning through unique features like quantum parallelism and entanglement. It delves into various QNN architectures, such as quantum circuits and hybrid quantum-classical models, showcasing their effectiveness in handling intricate computational tasks more efficiently than traditional neural networks. Furthermore, the article examines the current challenges and future prospects in this rapidly advancing field, emphasizing the pivotal role of QNNs in driving forward research in both quantum computing and artificial intelligence. Quantum neural networks are poised to not only enhance computational capabilities but also pave the way for groundbreaking innovations in diverse technological domains.
Artificial Intelligence and Quantum Computing are the latest Computing Technology of the 21st Century which has numerous applications in the field of Education, Entertainment, Healthcare, Security, and Commercial fields. Artificial Intelligence is more mature now compared to Quantum Computing and the latter one is still in its early stages and many top companies like Honeywell, Google, and IBM already invested a good amount of money in Research and development. Traditional Computing involves computation with a binary digit (bit) 1 and 0 whereas quantum computing involves Qubit and the information can have more than one or multiple states. Artificial Intelligence in simple words is making the computer system think, and respond to certain critical problems and provide the best solution like human beings. Quantum Computing is the traditional form of computing with new means or methods and which follows a similar rule of Atoms to manipulate or process Information. Artificial Intelligence system is strongly connected with the Internet of Things (IoT) for decision making and to provide the ideal solution when machines are connected with human beings. In this paper, we discuss on present and future developments of Artificial Intelligence and Quantum Computing and its contributions or relationship with other related fields of Information and Communication Technology (ICT). This paper also discusses future breakthroughs of these two technologies. This paper will make a real attempt to find the existing developments, future growth, and applications of Artificial Intelligence and Quantum Computing.
Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, and hybrid quantum neural networks, which combine classical neural-network components with quantum information processing units, have emerged as a practical framework for near-term quantum technologies. However, the rapid development of the field across diverse architectures, benchmarks and hardware assumptions makes it difficult to assess the utility of various proposals, identify where genuine advantages may arise, and determine how practitioners can use these models. While recent benchmarks caution that such gains have not yet been demonstrated at scale, theoretical work has identified tasks on which quantum models hold provable advantages, and hybrid approaches have delivered promising results on practical problems using deliberately compact quantum components and substantially fewer trainable parameters. Here, we review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities. We summarize their main theoretical and methodological foundations, survey some of the most promising architectures developed so far, and examine their implementation challenges and reported performance. By consolidating these perspectives, this review provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.