In this paper, we describe solar array monitoring using various machine learning methods including neural networks. We study fault detection using a quantum computer system and compare against results with a classical computer. We specifically propose a quantum circuit for a neural network implementation for Photovoltaic (PV) fault detection. The quantum circuit is designed for two qubits. Results and comparisons are presented for PV fault detection using a classical and quantum implementation of neural networks. In addition, simulations of a Quantum Neural Network are carried for a different number of qubits and results are presented for PV fault detection.
In this paper, we describe solar array monitoring using various machine learning methods including neural networks. We study fault detection using a quantum computer system and compare against results with a classical computer. We specifically propose a quantum circuit for a neural network implementation for Photovoltaic (PV) fault detection. The quantum circuit is designed for two qubits. Results and comparisons are presented for PV fault detection using a classical and quantum implementation of neural networks. In addition, simulations of a Quantum Neural Network are carried for a different number of qubits and results are presented for PV fault detection.
This paper explores the foundational concepts for the application of Quantum Machine Learning (QML) algorithms on photovoltaic (PV) array fault detection. Our study leverages in a unique manner quantum entanglement to enhance classification accuracy. We introduce new parameterized quantum circuit designs that enable flexible training and efficient feature encoding. We propose a novel approach utilizing quantum entanglement and quantum data correlation to encode complex relationships among PV features. This method enables the detection of subtle and correlated PV fault patterns. In addition, this approach highlights the potential of quantum systems to capture dependencies within data that are more challenging in classical computing models. In the designed quantum circuits, we show that as the number of entangling gates (two-qubit gates) grows, quantum entanglement can reveal data correlation. Simulation results examine the role of quantum data correlation in optimizing the classification process. Additionally, the scalability of entangled quantum circuits as the number of qubits increases is studied, along with the properties of quantum data correlation and its benefits in classification. The contributions presented in this paper are: a) the design of new quantum circuits that leverage correlation, b) the observation of a quantum state under different fault detection conditions, c) the capability to classify accurately among four different PV faults, and d) demonstration of accuracy improvement of about 10% over our previous study.
Positive unlabeled (PU) learning is a semi-supervised machine learning (ML) approach to a binary classification problem where a small subset of the data has positive labels, and the rest of the data is unlabeled. PU learning algorithms can be very efficient at reducing the computational cost of training ML classifiers because the labeling of data for supervised machine learning algorithms is time intensive. In this study, we develop new quantum PU (QPU) learning methods for photovoltaic (PV) fault detection and perform comparative analysis with classical PU learning methods. Quantum circuits for PU learning are designed and evaluated in this study. In addition, we examine various quantum machine learning (QML) and PU learning challenges including quantum measurement noise. As with many QML simulations, the presence of quantum noise and the heavy computational and memory requirements affect classification accuracy. Nevertheless, regardless of quantum noise effects and limited computational resources, our initial designs of quantum PU learning circuits achieved an accuracy score of 79.6%. This was achieved by implementing QPU learning with a quantum-classical hybrid neural network (QNN) architecture. In our study, we present simulation results with and without quantum noise models for comparison purposes.
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.
Combining the advantages of quantum computing and neural networks, quantum neural networks (QNNs) have gained considerable attention recently. However, because of the lack of quantum resource, it is costly to train QNNs. In this work, we presented a simple unsupervised method for quantum-classical convolutional networks to learn a hierarchy of quantum feature extractors. Each level of the resulting feature extractors consist of multiple quanvolution filters, followed by a pooling layer. The main contribution of the proposed approach is to use the K-means clustering to maximize the difference of quantum properties in quantum circuit ansatz. One experiment on the bearing fault detection task shows the effectiveness of the proposed method.
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.
Quantum machine learning techniques have been proposed as a way to potentially enhance performance in machine learning applications. In this paper, we introduce two new quantum methods for neural networks. The first one is a quantum orthogonal neural network, which is based on a quantum pyramidal circuit as the building block for implementing orthogonal matrix multiplication. We provide an efficient way for training such orthogonal neural networks; novel algorithms are detailed for both classical and quantum hardware, where both are proven to scale asymptotically better than previously known training algorithms. The second method is quantum-assisted neural networks, where a quantum computer is used to perform inner product estimation for inference and training of classical neural networks. We then present extensive experiments applied to medical image classification tasks using current state of the art quantum hardware, where we compare different quantum methods with classical ones, on both real quantum hardware and simulators. Our results show that quantum and classical neural networks generates similar level of accuracy, supporting the promise that quantum methods can be useful in solving visual tasks, given the advent of better quantum hardware.