Artificial Neural Networks

Akshay Bhuvaneswari Ramakrishnan, Pranav Manikandan, Karthikeyan Saminathan

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.

Péter Wittek

Avinash Chalumuri, Raghavendra Kune, B. S. B. S. Manoj

Artificial neural networks (ANN) proved to be efficient in solving many problems for big data analytics using machine learning. The complex and non-linear features of the input data can be learned and generalized by ANN. In the big data era, enormous amounts of data arrive from multiple sources. A stage is expected to be reached where even supercomputers are likely inundated with the big data. Training an ANN in such a situation is a challenging task due to the size and dimension of the big data. Also, a large number of parameters are to be used and optimized in the network to learn the patterns and analyze such data. Quantum computing is emerging as a field that provides a solution to this problem as a quantum computer can represent data differently using qubits. Qubits on quantum computers can be used to detect the hidden patterns in data that are difficult for a classical computer to find. Hence, there exists a huge scope for application in the area of artificial neural networks. In this work, we primarily focused on training an artificial neural network using qubits as artificial neurons. The simulation results show that our quantum computing approach for ANN (QC ANN) is efficient when compared to classical ANN. The model with qubits as artificial neurons can learn the features of data using fewer parameters for a binary classification task. We demonstrate our experiment using a quantum simulator and optimization of the quantum parameters used in QC ANN is carried out on a classical computer.

Péter Wittek

Charles R. Giardina

Xiangping Meng, Jianzhong Wang, Yuzhen Pi +1 more

Artificial Neural Networks (ANNs) are powerful tools that can be used to model and investigate various complex and non-linear phenomena. In this study, we construct a new ANN, which is based on Multi-Agent System (MAS) theory and quantum computing algorithm. All nodes in this new ANN are presented as Quantum Computational (QC) agents, and these agents have learning ability. A novel ANN training method was proposed via implementing QCMAS reinforcement learning. This new ANN has powerful parallel-work ability and its training time is shorter than classic algorithm. Experiment results show that this method is effective.