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.
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.
Abstract Learning from only positive and unlabeled (PU) data has broad applications in fields such as web data mining, product recommendations and medical diagnosis, which aims to train a binary classifier in the absence of negative labeled data. However, due to the lack of negative label information, prevailing PU learning methods usually rely on prior knowledge of unknown class distributions heavily. In fact, without additional constraints imposed by the prior knowledge, a direct learning strategy to coordinate the underlying clustering information in unlabeled data with the label information from positive training data is often considered challenging. To tackle this challenge, we propose a direct PU learning strategy using quantum formalization. By employing neural networks as backends, the samples are mapped into two-qubit composite systems, which should be understood here as mathematical entities encapsulating various classical distributions of two classical bits. Subsequently, the two qubits within the systems are trained to be as independent as possible from each other, capturing patterns of different classes. At the same time, their measurement results serving as the model outputs are encouraged to be maximally dissimilar. These characteristics enable effective training of classifiers on PU data. After formulating an appropriate discriminant rule, we introduce a quantum-inspired PU method named qPU using the direct learning strategy. This method not only has the potential to alleviate parameter sensitivity issues caused by prior estimation in other methods but is also straightforward to implement. Finally, experiments conducted on 13 classical datasets validate the effectiveness of qPU.
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.
Quantum machine learning (QML) provides a transformative approach to data analysis by integrating the principles of quantum computing with classical machine learning methods. With the exponential growth of data and the increasing complexity of computational tasks, quantum algorithms offer tremendous advantages in terms of processing speed, memory efficiency, and the ability to resolve issues intractable for classical systems. In this work, the use of QML techniques for both supervised and unsupervised learning problems is explored. Quantum-enhanced models such Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) show outstanding performance in classification and regression tasks by using quantum kernels and entanglement in supervised learning. Moreover, hybrid quantum-classical solutions offer useful implementations on noisy intermediate-scale quantum (NISQ) devices, hence bridging the gap between present quantum technology and practical uses. By means of comparative analysis, this paper emphasizes the possible benefits and drawbacks of QML, thereby providing understanding of its future importance in sectors including material science, finance, and healthcare. In the end, QML opens the path for a new era of intelligent data processing and solves until unthinkable difficult challenges.
Abstract Entanglement detection, the process of verifying quantum entanglement is a fundamental challenge in quantum information processing. Various approaches have been proposed to address this challenge, with many recent studies applying supervised machine learning methods. While these methods have demonstrated high accuracy in entanglement detection, it is reasonable to assume that the entangled states themselves are not definitively known. To address this limitation, we have devised a machine learning method for entanglement detection based on positive-unlabeled learning, a classical machine learning framework that does not use label information from negative data. Using a deep neural network model to synthetic dataset under the assumption of mixed states, we conducted experiments on a classical computer to valid the effectiveness and characteristics of the proposed method. Our approach introduces a novel framework that accounts for the data generation constraints in the training process of entanglement detector, thereby advancing machine learning techniques in quantum information science.
One of the most promising areas of research to obtain practical advantage is Quantum Machine Learning which was born as a result of cross-fertilisation of ideas between Quantum Computing and Classical Machine Learning. In this paper, we apply Quantum Machine Learning (QML) frameworks to improve binary classification models for noisy datasets which are prevalent in financial datasets. The metric we use for assessing the performance of our quantum classifiers is the area under the receiver operating characteristic curve AUC-ROC. By combining such approaches as hybrid-neural networks, parametric circuits, and data re-uploading we create QML inspired architectures and utilise them for the classification of non-convex 2 and 3-dimensional figures. An extensive benchmarking of our new FULL HYBRID classifiers against existing quantum and classical classifier models, reveals that our novel models exhibit better learning characteristics to asymmetrical Gaussian noise in the dataset compared to known quantum classifiers and performs equally well for existing classical classifiers, with a slight improvement over classical results in the region of the high noise.
Label propagation is an essential semi-supervised learning method based on graphs, which has a broad spectrum of applications in pattern recognition and data mining. This paper proposes a quantum semi-supervised classifier based on label propagation. Considering the difficulty of graph construction, we develop a variational quantum label propagation (VQLP) method. In this method, a locally parameterized quantum circuit is created to reduce the parameters required in the optimization. Furthermore, we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement, which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices. We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set, and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier. This work opens a new path to quantum machine learning based on graphs.