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.
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.
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.
Abstract This paper presents a groundbreaking application of Quantum Machine Learning (QML) in geophysics, specifically targeting seismic fault detection. By integrating quantum computing with machine learning, the study addresses critical limitations of traditional fault detection methods, such as the detection of subtle features, processing efficiency, and scalability. The primary objective is to enhance fault delineation accuracy and provide a robust framework suitable for large-scale seismic surveys and real-time applications. The methodology involves encoding seismic data into quantum states to efficiently handle high-dimensional features. A hybrid quantum-classical model is developed, utilizing quantum-enhanced algorithms for fault detection. This model incorporates Quantum Long Short-Term Memory (QLSTM) networks, which leverage quantum parallelism and interference to enhance the processing capabilities of traditional LSTM networks. The QLSTM architecture employs quantum gates and memory cells to maintain and regulate the flow of quantum information, enabling more efficient and accurate processing of sequential data. The workflow encompasses quantum-based feature extraction, model training with seismic datasets, and validation using both synthetic and real-world examples. Comparative studies with traditional machine learning methods assess performance metrics, including accuracy, noise suppression, and computational efficiency. Quantum hardware simulators ensure the method's feasibility for practical applications. Results demonstrate that QML, powered by QLSTM, significantly outperforms conventional methods in seismic fault detection, achieving higher accuracy, better resolution of subtle geological features, and reduced noise sensitivity. Comprehensive testing on synthetic and field datasets reveals QML's ability to capture complex fault patterns, particularly in geologically challenging settings. Additionally, QML's computational efficiency enables rapid analysis of large datasets, facilitating timely decision-making. In conclusion, QML emerges as a superior approach for seismic fault detection, addressing longstanding challenges and offering novel opportunities for real-time seismic interpretation. By leveraging quantum computing, QML provides unprecedented insights into subsurface structures, leading to more accurate predictions and informed decisions in exploration and production operations. This paper introduces a transformative approach to seismic fault detection, contributing significantly to existing literature and offering actionable insights for the petroleum industry, particularly in subsurface imaging and reservoir characterization.
This paper explores the application of quantum computing to the critical tasks of feature selection and dimensionality reduction within machine learning. Traditional methods often struggle with high-dimensional data, leading to computational bottlenecks and potentially reduced model accuracy. We propose a novel approach leveraging the principles of quantum error correction and quantum entanglement to construct an algorithm capable of efficiently identifying and reducing the most relevant features. The core mechanism utilizes quantum machine learning techniques, specifically quantum feature mapping and feature extraction, to transform data into a quantum state that inherently captures feature relationships. This allows for a more effective selection process based on quantum interference and the exploration of complex correlations. We demonstrate, theoretically, the potential advantages of this approach over classical methods, highlighting improvements in both efficiency and predictive accuracy. The algorithm's design incorporates error correction to mitigate the effects of noise inherent in quantum systems, further enhancing its robustness. The research contributes to the growing field of quantum machine learning, offering a new paradigm for tackling complex data challenges.
Image classification plays a vital role in large-scale data analysis, especially in object recognition tasks using advanced Deep Learning (DL) frameworks. However, the growing complexity and computational demands of modern DL models have introduced challenges related to scalability and efficiency. Quantum Computing (QC) has emerged as a promising alternative, capable of addressing these limitations by leveraging the principles of Quantum Machine Learning (QML). However, many existing QML models require a large number of qubits, which poses limitations within the current Noisy Intermediate-Scale Quantum (NISQ) era. This work introduces a novel Adaptive Quantum Convolutional Neural Network (AQCNN) designed for efficient and scalable image classification within the constraints of NISQ devices. Addressing the limitations of existing QML approaches, particularly the high qubit requirements, AQCNN incorporates a resource-efficient quantum convolutional layer that performs localized quantum filtering using parameterized quantum circuits. A classical preprocessing layer encodes input features to reduce qubit load, followed by quantum embedding and hybrid quantum-classical layers that optimize feature extraction and classification performance. The model leverages an adaptive quantum convolution strategy, minimizing quantum gate depth and circuit complexity while preserving critical spatial hierarchies in image data. Evaluated on benchmark datasets, AQCNN achieved 95.88% accuracy on MNIST and 95.68% on FMNIST, outperforming comparable QML architectures. Additionally, the model supports scalable execution through parallel quantum circuit arrays, enabling practical deployment on current quantum hardware. This architecture demonstrates a significant advance in quantum-assisted image classification, balancing performance with qubit and gate efficiency. The integration of adaptive quantum convolution and hybrid processing not only enhances classification accuracy but also provides a viable path forward for deploying QML solutions under realistic hardware constraints.
Quantum Machine Learning (QML) represents a paradigm shift in computational approaches, offering the potential to surpass classical machine learning algorithms in complex data analysis. This paper investigates the application of QML to the identification of non-linear patterns, focusing on leveraging quantum states to uncover hidden correlations and anomalies within datasets. We propose a novel framework that utilizes quantum entanglement and superposition to enhance pattern recognition capabilities. The core mechanism centers on harnessing the inherent properties of quantum states to provide a fundamentally different approach to data analysis, specifically designed to address the challenges posed by non-linear data. This research explores the potential of quantum algorithms to improve pattern detection and anomaly detection, and investigates the influence of quantum characteristics on pattern recognition processes. The paper will detail the implementation of a quantum model for pattern recognition, discuss its performance compared to classical models, and explore the fundamental principles underpinning its effectiveness. We argue that this work introduces a new perspective on pattern recognition, offering the possibility of enhanced accuracy, robustness, and the ability to identify subtle patterns within complex datasets. The core claim posits that quantum machine learning can effectively identify and interpret non-linear patterns, offering significant advantages over conventional methods.