Mitigating High Dimensionality and Few Training Data with a Quantum Kernel Learning Approach

doi:10.1007/978-3-032-15984-7_13

Mitigating High Dimensionality and Few Training Data with a Quantum Kernel Learning Approach

2026-01-01

0

PRE

AI

DOI：10.1007/978-3-032-15984-7_13

Original

Original request for help

Save

Abstract

En 中文

In this work, we investigate the integration of quantum algorithms as kernel functions in Support Vector Machines (SVMs) to classify highly imbalanced datasets, using a case study on anomaly detection in wind turbine sensor data. A hybrid quantum-classical computational strategy was developed and benchmarked against four classical kernels, demonstrating superior or competitive performance in several scenarios. The evaluation focused on the model's generalization ability to detect minority classes in predictive maintenance datasets. The main contributions of our research include: (i) the impact of kernel complexity on minority class detection, (ii) the comparative performance of quantum and classical models in identifying fault types, and (iii) the influence of different quantum entanglement strategies on SVM performance. Simulation results using the Qiskit (Quantum Information Software Kit) framework highlight how superposition and entanglement can effectively map data into higher-dimensional spaces, improving hyperplane separation and enhancing fault detection.

Keywords:

imbalanced data

quantum computing

quantum kernels

quantum machine learning

support vector machine

Mitigating High Dimensionality and Few Training Data with a Quantum Kernel Learning Approach

Abstract

Journal

Organization