Photovoltaic fault classification by leveraging quantum entanglement and correlation

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.