Quantum Kernel for Image Classification of Real World Manufacturing Defects (2212.08693v1)

Abstract: 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.

• The paper demonstrates that quantum kernel methods can outperform classical SVMs on simulators in classifying low-resolution manufacturing defects.
• It employs Angle and IQP encoding techniques, with Angle Encoding showing promising results on simulators yet variable performance on actual QPUs.
• The study highlights the need for enhanced error mitigation and optimized encoding strategies to better harness quantum advantages in real-world image classification.

Quantum Kernel for Image Classification of Real-World Manufacturing Defects

The paper "Quantum Kernel for Image Classification of Real-World Manufacturing Defects" investigates the applicability of quantum kernel methods for the classification of low-resolution manufacturing defect images, contrasting this novel approach with classical support vector machines (SVMs). The paper leverages quantum kernel techniques, implemented on both quantum simulators and actual quantum processing units (QPUs), to explore their potential in achieving tasks that have traditionally been the domain of classical machine learning algorithms.

Summary of Methods and Implementation

The paper utilizes quantum kernel methods, which theoretically outperform classical SVMs in scenarios involving intricate data features and non-linear relationships. Specifically, two encoding methods for data quantum conversion were explored: Angle Encoding and Instantaneous Quantum Polynomial (IQP) Encoding. The manufacturing defect images used, sourced from The Smart Factory @ Wichita, were preprocessed using techniques such as Principal Component Analysis (PCA) to conform to the computational limitations of the QPU in use.

Dynamic decoupling (DD) via the Mitiq package was employed as an error mitigation strategy, implemented uniquely within this research for image classification through quantum kernels. Notably, the paper emphasizes the variance in encoding effectiveness, with Angle Encoding exhibiting the most promising results on simulators but presenting inconsistent performance across actual QPU executions.

Results and Observations

On simulators, the quantum kernel methods surpassed classical SVMs in the classification of manufacturing defect images across both angle and IQP encodings. However, the results on the QPU revealed a stark contrast in performance consistency. Angle Encoding returned superior results in one out of three QPU runs but suffered from significant variability in outcomes. IQP encoding, despite its consistency, failed to exceed the classical approach in practical QPU applications. The Mitiq DD error mitigation—while anticipated to alleviate noise-related issues—did not provide the expected improvements, indicating avenues for further investigation into alternative error-correction mechanisms.

Implications and Future Research Directions

The findings presented highlight the potential yet unclear role of quantum kernel methods in practical image classification tasks. With manufacturing image data posing unique challenges due to its non-linear complexities, quantum approaches offer a new dimension of data handling in higher-dimensional Hilbert spaces.

The paper's inconsistent results under different experimental conditions underscore the necessity for advancements in error mitigation and encoding strategies. Alternative methods beyond Mitiq DD, particularly compatible with emerging QPU technologies, warrant exploration. Additionally, the demonstrated issues with noise coherence in quantum hardware necessitate ongoing research to refine existing methodologies.

Future investigations could expand these quantum kernel methods to domains such as medical imaging, where data complexity similarly challenges classical systems. Enhancements in QPU design, alongside breakthroughs in quantum error correction, could further potentiate these efforts, maximizing the utility of quantum resources in data-rich environments.

Conclusion

This paper contributes to evolving computational paradigms by juxtaposing quantum kernel methods against classical machine learning techniques for real-world applications. It serves as a precursor to subsequent inquiries that may leverage both the structured and unstructured data strengths offered by quantum computational capabilities, advancing the field's understanding of quantum advantage in practical machine learning tasks.