  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. The manufacturing images were used to
determine if a product was defective or was produced correctly through the
manufacturing process. We also tested the Mitiq dynamical decoupling (DD)
methodology to understand effectiveness in decreasing noise-related errors. We
also found that the way classical data was encoded onto qubits in quantum
states affected our results. All three quantum processing unit (QPU) runs of
our angle encoded circuit returned different results, with one run having
better than classical results, one run having equivalent to classical results,
and a run with worse than classical results. The more complex instantaneous
quantum polynomial (IQP) encoding approach showed better precision than
classical SVM results when run on a QPU but had a worse recall and F1-score. We
found that DD error mitigation did not improve the results of IQP encoded
circuits runs and did not have an impact on angle encoded circuits runs on the
QPU. In summary, we found that the angle encoded circuit performed the best of
the quantum kernel encoding methods on real quantum hardware. In future
research projects using quantum kernels to classify images, we recommend
exploring other error mitigation techniques than Mitiq DD.
