Classical neural networks (CNNs) provide wide applications in communication technology, ranging from enhancement of key performance indicators (KPIs) of communication protocols to utilities in intelligent networks. Quantum neural networks (QNNs) have advantages over CNNs for applications in future communication networks. This article surveys existing methods for implementing QNNs and suggests a novel resource-friendly quantum system with enhanced activation for better performance in QNNs. Our approach is motivated by a principle of neuronal gain control established in neuroscience and was implemented on a quantum computer using IBM's qiskit. Potential implications of such a model for communication technology are discussed.
Classical neural networks (CNNs) provide wide applications in communication technology, ranging from enhancement of key performance indicators (KPIs) of communication protocols to utilities in intelligent networks. Quantum neural networks (QNNs) have advantages over CNNs for applications in future communication networks. This article surveys existing methods for implementing QNNs and suggests a novel resource-friendly quantum system with enhanced activation for better performance in QNNs. Our approach is motivated by a principle of neuronal gain control established in neuroscience and was implemented on a quantum computer using IBM's qiskit. Potential implications of such a model for communication technology are discussed.
Quantum Neural Networks (QNNs) have emerged as a promising avenue in the quest for next-generation machine learning capabilities.By harnessing the principles of quantum mechanics and quantum computing, QNNs offer the potential for exponential speedup and enhanced computational power compared to classical machine learning algorithms.This paper presents a comprehensive exploration of Quantum Neural Networks, covering their foundational concepts, various architectures, novel quantum-enhanced machine learning algorithms, practical implementations, advantages, and challenges.We begin with an overview of related work in Quantum Machine Learning, highlighting the achievements and progress in the field.Subsequently, we delve into the fundamental principles of Quantum Mechanics and Quantum Computing to provide a solid grounding for understanding QNNs.We then introduce the basics of Quantum Neural Networks, including quantum neurons, quantum gates, and quantum circuits for machine learning tasks.Various QNN architectures, such as Variational Quantum Circuits and Quantum Convolutional Neural Networks, are explored along with their potential applications.Additionally, quantum-enhanced machine learning algorithms, like Quantum Support Vector Machines and Quantum Generative Adversarial Networks, are discussed for a deeper understanding of QNN capabilities.Practical aspects, including hardware implementations, noise mitigation, and experimental results, are presented to shed light on the feasibility of QNNs in real-world scenarios.The paper concludes by discussing the advantages and challenges of Quantum Neural Networks and their potential implications for the future of machine learning and artificial intelligence.
In this paper, we present an overview of quantum neural network (QNN) and quantum computing benefits in the Artificial Intelligence field with methods used to enhance the application of machine learning. The approach uses quantum computers to enhance Convolutional Neural Network (CNN) with Hybrid QNNs using IBM Qiskit called the Convolutional Neural Network Hybrid Qiskit (CNNHQ). This paper will compare and focus on using the quantum concepts of computation to develop and optimize the network process entirely depending on Activation functions used in the neural network approach.
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.
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.