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 were 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...
Saved in:
| Main Authors: | José Miguel Monzón-Verona, Santiago García-Alonso, Francisco Jorge Santana-Martín |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/4/1277 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
IQGO: Iterative Quantum Gate Optimiser for Quantum Data Embedding
by: Tautvydas Lisas, et al.
Published: (2024-01-01) -
Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks
by: Diego Alvarez-Estevez
Published: (2025-01-01) -
Satellite image classification with neural quantum kernels
by: Pablo Rodriguez-Grasa, et al.
Published: (2025-01-01) -
Enhancing Text Classification Through Quantum Transfer Learning: A Hybrid Quantum-Classical Approach With Complex Kernel Self-Attention Networks
by: Xiaoxiao Chen, et al.
Published: (2025-01-01) -
QKDTI A quantum kernel based machine learning model for drug target interaction prediction
by: Gundala Pallavi, et al.
Published: (2025-07-01)