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, 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.
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, 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.
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.
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.
Quantum computing and neural networks have revolutionized computational intelligence and have the potential to bring revolution in a wide range of scientific, industrial, and technological fields. In some computational activities, quantum computing utilizes the concepts of superposition, entanglement, and quantum parallelism and performs better than classical computers by processing complicated data with high speed and efficiency. Neural networks have transformed decision-making, natural language processing, and pattern recognition especially deep learning models. By incorporating quantum computing into neural network architectures, Quantum Neural Networks (QNNs) open new paradigms in data encoding, feature representation, and learning dynamics by overcoming the computational constraints and scaling related problems of classical models. In this chapter, the theoretical basis, latest developments and possible applications of a quantum neural network have been discussed. This chapter covers the constraints of near-term quantum devices, quantum noise, data encoding techniques, and practical difficulties in hardware implementation. Real-world applications in quantum chemistry, financial modeling, cryptography, and biological imaging have been studied. To fully realize the promise of quantum neural processing, this chapter concludes by describing current research shortcomings and providing opportunities for multi-disciplinary collaboration among computer scientists, physicists, and AI researchers.
The field of Quantum Artificial Intelligence (Quantum AI) has emerged as a promising interdisciplinary research area where the computational power of quantum computing is merged with the learning and decision-making power of AI. The application of quantum principles such as superposition, entanglement and quantum interference to machine learning algorithms has created new possibilities for solving problems that may be difficult to solve by classical machines. In the last few years, advances in Noisy Intermediate-Scale Quantum (NISQ) devices have helped to speed advances in Quantum Machine Learning (QML) algorithms, Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Quantum Convolutional Neural Networks (QCNNs) and hybrid learning algorithms. This review provides a comprehensive overview of the research in the field of Quantum AI from 2020 to 2026, covering the key concepts, novel architectures, algorithmic advancements, and practical applications. A critical assessment of the application of Quantum AI in fields such as healthcare, finance, cybersecurity, scientific computing and optimization problems is provided, and the benefits of quantum-enhanced learning models are discussed. In addition, common difficulties such as quantum hardware limitations, sensitivity to noise, encoding of data, scalability constraints and a lack of speed in training variational quantum circuits are discussed. The review also covers new avenues of research, including trustworthy quantum machine learning, explainable Quantum AI, quantum federated learning, and fault-tolerant quantum computing. The results suggest while practical large-scale deployment is currently limited by existing hardware capabilities, hybrid quantum-classical systems have great potential to rapidly move intelligent data processing and develop next-generation AI systems.