Quantum Kernel for Image Classification of Real World Manufacturing Defects

Understand

The quantum kernel method results clearly outperformed a classical SVM when analyzing low-resolution images with minimal feature selection on the quantum simulator, with inconsistent results when run on an actual quantum processor.

• We chose to use an existing quantum kernel method for classification.
• We applied dynamic decoupling error mitigation using the Mitiq package to the Quantum SVM kernel method, which, to our knowledge, has never been done for quantum kernel methods for image classification.
• We applied the quantum kernel method to classify real world image data from a manufacturing facility using a superconducting quantum computer.