Presentation: Quantum Machine Learning Methods for Semiconductor Wafer Defect Classification

These are slides from a May 2026 presentation on benchmarking Quantum Machine Learning (QML) methods against classical Support Vector Machines (SVMs) for the task of classifying semiconductor wafer defects

This presentation discusses:

  • The motivation behind QML and its presumed advantages over classical AI/ML techniques

  • The industrial dataset used for classification tasks, including all data processing steps

  • Results, benchmarks, and future directions for research and development

Slides can be found here (PDF) and here (PPT). There is also an accompanying written report.

Next

Jones Calculus in Python for Electro-Optical Polarization Devices