Artificial neural networks (ANNs) have gained significant attention in various fields due to their ability to learn from data and extract meaningful patterns. Quantum machine learning (QML) has opened new opportunities for ANNs, with the advent of quantum computing. This chapter provides an introduction to the use of ANNs in QML, discussing fundamentals of QML and quantum mechanics, adaptability of ANN components, quantum-inspired classical ANNs, quantum perceptrons, activation functions, quantum hidden layers, backpropagation, quantum convolutional neural networks (CNNs), and quantum feed-forward neural networks (FNNs). The chapter also discusses the potential impact of quantum ANNs on industries and research disciplines, including quantum chemistry, quantum error correction, and quantum machine learning for quantum computers. The chapter also discusses limitations of quantum ANNs, such as noise and the need for large-scale quantum processors, and future directions for quantum ANNs, such as hybrid models and quantum neural networks with machine learning acceleration.
Artificial neural networks (ANNs) have gained significant attention in various fields due to their ability to learn from data and extract meaningful patterns. Quantum machine learning (QML) has opened new opportunities for ANNs, with the advent of quantum computing. This chapter provides an introduction to the use of ANNs in QML, discussing fundamentals of QML and quantum mechanics, adaptability of ANN components, quantum-inspired classical ANNs, quantum perceptrons, activation functions, quantum hidden layers, backpropagation, quantum convolutional neural networks (CNNs), and quantum feed-forward neural networks (FNNs). The chapter also discusses the potential impact of quantum ANNs on industries and research disciplines, including quantum chemistry, quantum error correction, and quantum machine learning for quantum computers. The chapter also discusses limitations of quantum ANNs, such as noise and the need for large-scale quantum processors, and future directions for quantum ANNs, such as hybrid models and quantum neural networks with machine learning acceleration.
Quantum neural networks (QNNs) are a revolutionary paradigm placed at the frontier of machine learning and quantum computing. By utilizing quantum mechanical principles like superposition, entanglement, and quantum parallelism, QNNs facilitate the solution of complex machine learning problems that were thought to be impossible according to conventional models. This chapter provides a detailed study of QNNs, from elementary principles to practical applications. It begins with a rundown of the most significant concepts in machine learning and quantum computing, followed by QNN architecture, with important points including quantum gates and parameterized quantum circuits. The chapter addresses challenges in training QNNs, with special attention given to hybrid quantum-classical optimization algorithms and their utility in successful model training. It also examines multiple applications of QNNs, including simulation in quantum chemistry, optimization problems, and real-world machine learning applications, to demonstrate how they have the potential to outperform conventional algorithms in high dimensional or highly entangled data environments. This chapter provides a comparative assessment of quantum machine learning frameworks, quantum hardware platforms, and hybrid optimization methods and aids in determining the most suitable tools to design QNNs. It finally discusses possible areas of future research and research issues in the field, emphasizing the urgent need to improve quantum hardware, increase noise tolerance, and scale up. As quantum computing technology evolves, QNNs are expected to be at the center of solving some of the most complex and computationally intensive problems encountered in various industries.
Quantum Machine Learning is gaining prominence with the advent of real quantum processors. The quantum analogue of one of the most important and popular model of machine learning, namely Artificial Neural Network is studied and reviewed here in this paper. Various approaches to design and implement a quantum neural network has been studied. Most of the works done in this field involved a translation of classical neural network components into the language of quantum physics. But direct translation cannot be a solution as not every classical component has a quantum analogue. Non-linear activation functions are good examples of it. Quantum operators need to be linear and therefore finding alternate solutions to activation functions is an active area of research. Nevertheless, its advantages over the classical models are profound. Quantum superposition gives exponential storage capacity to a quantum network. Quantum parallelism can be put to use and to train the network with multiple inputs in one go. Despite being a new field, many real world applications of a quantum neural network has also been theorised. These models, their advantages, applications and limitations has been discussed in this paper.
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.
Quantum neural networks (QNNs) represent a cutting-edge approach to machine learning and artificial intelligence, harnessing the principles of quantum mechanics to process and analyze data in fundamentally new ways. Unlike classical neural networks, which operate on classical bits and are limited by the laws of classical physics, QNNs use quantum bits (qubits) and exploit quantum phenomena such as superposition and entanglement to achieve unprecedented computational power and efficiency. This chapter explores the principles and applications of QNNs, including their potential for solving complex optimisation problems, pattern recognition tasks, and quantum simulation tasks. We discuss various QNN architectures, training algorithms, and applications, highlighting their advantages and challenges compared to classical neural networks. Additionally, we explore the potential impact of QNNs on fields such as quantum computing, quantum communication, and quantum sensing, as well as their implications for society and the economy. Overall, this chapter provides insights into the emerging field of quantum neural networks and sets the stage for further research and innovation in this exciting area.
Quantum Neural Networks (QNNs) represent a novel computational paradigm that merges the principles of quantum computing with the architecture of artificial neural networks. Through the quantum phenomena of superposition, entanglement, and interference, QNNs enable parallel computation in high-dimensional Hilbert spaces, offering the potential to surpass the representational limits of classical models. This review provides a comprehensive overview of the theoretical foundations and architectures of QNNs, including Quantum Perceptrons, Variational Quantum Circuits (VQCs), Quantum Convolutional Neural Networks (QCNNs), and Quantum Recurrent Neural Networks (QRNNs). Furthermore, it discusses hybrid quantum–classical training mechanisms and key challenges such as barren plateaus, decoherence, and sampling complexity. The review also highlights recent applications of QNNs in medical diagnostics, materials science, and financial forecasting, demonstrating their potential to accelerate computation and improve predictive accuracy. Finally, future research directions are discussed in relation to computational efficiency, model interpretability, and integration with next-generation quantum hardware.
This chapter offers a clear and accessible introduction to the field of quantum-inspired neural computing. This area combines ideas from quantum mechanics with traditional neural network models to create new possibilities in machine learning and optimization metaheuristic algorithms. Unlike actual quantum computing, quantum-inspired approaches work on classical computers but borrow key concepts like superposition, entanglement, and probabilistic behaviour to improve how neural networks learn and process information. The study begins by explaining why researchers are interested in using quantum principles in neural networks. It then introduces the basic mathematical ideas behind these concepts, such as quantum states and transformations, and shows how they can be used to design more powerful and flexible learning systems. It focuses on discussing current models, outlining the distinctions between quantum-inspired and true quantum systems, and evaluating the potential benefits and challenges of this approach. In addition, Quantum-inspired neural computing introduces a novel framework that bridges classical artificial intelligence with principles derived from quantum mechanics. By simulating quantum behaviours on classical hardware, it opens new avenues for developing more adaptive and efficient learning models. This emerging approach offers promising advantages in areas such as optimization, parallel processing, and high-dimensional data representation. Finally, despite its potential, the field faces notable limitations and challenges. These include the lack of limited theoretical formalization, and difficulties in scaling models for complex real-world applications. Moreover, distinguishing genuine quantum-inspired advantages from conservative algorithmic improvements remains an current research apprehension.