Quantum Variational vs Quantum Kernel Machine Learning Models for Partial Discharge Classification in Dielectric Oils

In this paper, electrical discharge images are classified using AI with quantum machine learning techniques. These discharges are originated in dielectric mineral oils and were detected by a high-resolution optical sensor. The captured images were processed in a Scikit-image environment to obtain a reduced number of features or qubits for later training of quantum circuits. Two quantum binary classification models were developed and compared in the Qiskit environment for four discharge binary combinations. The first was a quantum variational model (QVM), and the second a conventional support vector machine (SVM) with a quantum kernel model (QKM). The execution of these two models was realized on three fault-tolerant physical quantum IBM computers. In the QVM, with two qubits, an accuracy of 92% was observed in the first discharge combination in the three quantum computers used, with a margin of error of 1% compared to the simulation obtained on classical computers. For the other three discharge combinations, the test set accuracy was around 88%. In the SVM-QKM, with two qubits, the test set accuracy for the first discharge combination reached 97%, while for the other three combinations the average accuracy was 89%. In addition, in this model, increasing the number of qubits to eight showed an improvement in accuracy to around 99% for the first combination and to an average of 97% for the other three combinations, demonstrating that the model improves using more features.