Quantum computing is a novel paradigm that promises to be the future of computing. The performance of quantum algorithms has proved to be stunning. ANN within the context of classical computation has been used for approximation and classification tasks with some success. This paper presents an idea of quantum neural networks along with the training algorithm and its convergence property. It synergizes the unique properties of quantum bits or qubits with the various techniques in vogue in neural networks. An example application of Fisher’s Iris data set, a benchmark classification problem has also been presented. The results obtained amply demonstrate the classification capabilities of the quantum neuron and give an idea of their promising capabilities.
Quantum computing is a novel paradigm that promises to be the future of computing. The performance of quantum algorithms has proved to be stunning. ANN within the context of classical computation has been used for approximation and classification tasks with some success. This paper presents an idea of quantum neural networks along with the training algorithm and its convergence property. It synergizes the unique properties of quantum bits or qubits with the various techniques in vogue in neural networks. An example application of Fisher’s Iris data set, a benchmark classification problem has also been presented. The results obtained amply demonstrate the classification capabilities of the quantum neuron and give an idea of their promising capabilities.
In this paper, integrated quantum neural network (QNN), which is a class of feedforward neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that (QNN’s) are capable of recognizing structures in data, a property that conventional (FFNN’s) with sigmoidal hidden units lack. In addition, (QNN) gave a kind of fast and realistic results compared with the (FFNN). Simulation results indicate that QNN is superior (with total accuracy of 97.778%) than ANN (with total accuracy of 93.334%).
Neural networks have attracted much interest in the two decades for their potential to describe brain function realistically. Quantum computing is a likely candidate for improving the computational efficiency of neural networks, since it has been very successful in doing so for a selected set of computational problems. We have proposed Qubit neural network that is a multilayered neural network composed of Qubit inspired neurons with Quantum Back Propagation learning and confirmed the performance concerning basic benchmark problems such as 4-bit and 6-bit parity check problems. In this paper, we examine this Qubit neural network through more practical problems, for example, the Iris data classification and the night vision processing.
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.
Abstract Quantum neural networks (QNNs) are emerging as a promising paradigm at the intersection of quantum computing and artificial intelligence. However, their conceptual abstraction and physical implementation remain challenging in educational contexts. In this work, we present a didactic framework to teach the fundamentals of QNNs by leveraging classical artificial neural networks (ANNs), Python simulation with Qiskit, and analysis of training dynamics using the Iris dataset. The paper begins with a theoretical comparison between classical and quantum neurons, followed by the design of a full QNN training algorithm implemented in Python. Through simulated results and pedagogical discussion, we show how learners can explore key aspects of quantum learning—including superposition, variational circuits, probabilistic measurement, and optimization—without access to quantum hardware. The use of graphical outputs and code-structure correspondence enhances student engagement and promotes algorithmic thinking. This approach supports quantum science education by offering a reproducible, interactive, and conceptually aligned method for introducing QNNs in undergraduate and graduate classrooms.
Quantum neural networks, which combine quantum computing and machine learning, have become a topic of great interest due to the advancement of quantum computing. This paper introduces a pure quantum neural network model based on parameterized quantum circuits, applied to image classification using the Iris dataset. Experimental validation demonstrates superior classification accuracy in three-class tasks. Specifically, this paper innovatively uses IQP encoding to encode classical data into quantum states, and independently designs the Ansatz in the quantum neural network. Experimental results show that compared with the other three classical classification algorithms and four quantum neural network models, the pure quantum neural network model proposed in the study has the highest accuracy in image classification tasks. In addition, this article analyzes and evaluates the model’s expression characteristics and entanglement capabilities, providing a reference for further optimizing and expanding the quantum neural network model. Overall, this research provides new ideas and methods for the integration of quantum computing and machine learning, which has certain practical value and research significance.
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.