This dissertation weds three specialties---Evolutionary Computation (EC), Neural Networks (NN), and Computing (QC)---to form a new triple Quantum Evolution of Neural Networks (QENN). After several review chapters on the principles of evolutionary algorithms, neural networks, quantum computing, etc., quantum genetic operators are defined, and several new pure and hybrid quantum/classical evolutionary algorithms are introduced that are considerably faster than their classical equivalents. Several concrete examples of the quantum evolution of very simple neural networks are also provided. Another approach to quantum neural network evolution, called Neuron Evolution (QNE), is introduced.
This dissertation weds three specialties---Evolutionary Computation (EC), Neural Networks (NN), and Computing (QC)---to form a new triple Quantum Evolution of Neural Networks (QENN). After several review chapters on the principles of evolutionary algorithms, neural networks, quantum computing, etc., quantum genetic operators are defined, and several new pure and hybrid quantum/classical evolutionary algorithms are introduced that are considerably faster than their classical equivalents. Several concrete examples of the quantum evolution of very simple neural networks are also provided. Another approach to quantum neural network evolution, called Neuron Evolution (QNE), is introduced.
Quantum Neural Networks (QNNs) represent a fascinating intersection of Quantum Computing and Artificial Neural Networks.Researchers explored combining quantum computing and neural networks in the 1990s and early 2000s.Quantum Neural Networks (QNNs) combine principles from quantum computing and artificial neural networks to potentially revolutionize machine learning.QNNs are neural networks that utilize quantum computing principles, such as superposition, entanglement, and quantum parallelism, to process and transform information.With Quantum Superposition QNNs can represent complex probability distributions, allowing for more nuanced modeling of uncertainty.While with Quantum Entanglement, QNNs can capture complex correlations between inputs, enabling more accurate modeling of complex relationships.Apart of that Quantum Parallelism facilitates QNN to process multiple inputs simultaneously, potentially speeding up certain computations.QNNs can potentially solve certain problems faster than classical neural networks.QNNs can also capture complex relationships and patterns, potentially leading to improved accuracy and can provide new insights into complex systems and phenomena.QNNs hold promise for advancing machine learning and solving complex problems.Ongoing research aims to overcome challenges and unlock their full potential.Quantum-Classical hybrid approaches were introduced, combining the strengths of both paradigms.This research paper presents an exploratory and critical survey of the current status of Quantum Neural Networks, Quantum Computing, along with the potential applications of QNNs in Artificial Intelligence.
This work presents a novel fundamental algorithm for for defining and training Neural Networks in Quantum Information based on time evolution and the Hamiltonian. Classical Neural Network algorithms (ANN) are computationally expensive. For example, in image classification, representing an image pixel by pixel using classical information requires an enormous amount of computational memory resources. Hence, exploring methods to represent images in a different paradigm of information is important. Quantum Neural Networks (QNNs) have been explored for over 20 years. The current forefront work based on Variational Quantum Circuits is specifically defined for the Continuous Variable (CV) Model of quantum computers. In this work, a model is proposed which is defined at a more fundamental level and hence can be inherited by any variants of quantum computing models. This work also presents a quantum backpropagation algorithm to train our QNN model and validate this algorithm on the MNIST dataset on a quantum computer simulation.
Using quantum concepts like superposition and entanglement, quantum-enhanced neural networks (QENN) combine machine learning (ML) and quantum computing to improve neural network performance. They are expected to have a revolutionary influence on optimization, pattern recognition, and complex data analysis. In this work, authors discuss the principles of quantum computing, including superposition, entanglement, and quantum gates, before introducing many quantum ML (QML) techniques, including Grover’s search, Shor’s factoring, and quantum annealing. Following that, it discusses the fundamentals of neural networks for problems like image recognition and natural language processing while addressing issues with big data and high-dimensional features. This work focuses on using quantum processors to speed up traditional ML tasks by combining quantum algorithms with neural networks to generate hybrid models. It investigates quantum kernel techniques, quantum variational algorithms, and QENN, demonstrating their potential to improve conventional neural network frameworks. In addition to this authors also include real-world applications in fields including drug development, materials research, finance, and optimization to show the revolutionary effects of quantum-enhanced machine learning (QEML) on current problems. In conclusion, this chapter provides a thorough review of QML, highlighting the synergy between quantum algorithms and neural networks. It offers unique insights into the field’s current state, practical applications, and hopes for defining the future of artificial intelligence through quantum computational paradigms.
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.
This paper shows how the new field of quantum computing can be applied to the creation of neural network circuits. Quantum computing is capable of processing huge numbers of quantum states simultaneously, in parallel, ("quantum parallelism"). In theory, QC ought to be able to process all possible points in a 2/sup N/ search space (of N bit bit-strings). If N is large, 2/sup N/ is gigantic, making a systematic search with a classical computer impossible. This paper presents an extended example of a simple QC algorithm applied to the generation of neural network circuits.
Quantum computing and deep learning are increasingly becoming an emerging paradigm that can transform the way complex computational problems are approached. This chapter offers an overview of Quantum Neural Networks and Deep Learning, presenting material on how quantum mechanics can be merged with traditional neural networks. Quantum computing principles, including those of superposition, entanglement, and quantum parallelism, offer a very promising toolkit to overcome some of the big issues being faced today--such as high-dimensional data processing, faster optimization, and reduced computation complexity. The chapter describes the architecture and operation of QNNs, compares them with classical deep learning models, and points out their applications in drug-discovery-based research, image recognition, and cryptography. This chapter also considers existing barriers, such as hardware limitations and noise in quantum systems, and sheds some light on the ongoing research efforts and future avenues in hybrid quantum-classical models. Additionally, the chapter gave a detailed insight into bridge quantum theory and artificial intelligence and provided a simple yet in-depth foundation for anyone interested in the quantum augmentation of deep learning's transformative possibilities.