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 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 widespread benefits of classical machine learning along with promised speedups by quantum algorithms over their best performing classical counterparts have motivated development of quantum machine learning algorithms that combine these two approaches. Quantum Kernel Methods (QKMs) [22, 49] describe one such combination, which seeks to leverage the high dimensional Hilbert space over quantum states to perform classification on encoded classical data. In this work I present an analysis of QKM algorithms used to encode and classify real data using a quantum processor, aided by a suite of custom noise models and hardware optimizations. I introduce and validate techniques for error mitigation and readout error correction designed specifically for this algorithm/hardware combination. Though I do not achieve high accuracy with one type of QKM-based classifier, I provide evidence for possible fundamental limitations to the QKM as well as hardware limitations that are unaccounted for by a reasonable Markovian noise model.
In recent years, the diversification of consumer values has led to an increase in the number of small-quantity, high-mix products. For many manufacturing companies, shipping inspections of such products are of great importance. As all products have the same value, good and defective products need to be efficiently identified. Now, a promising future application of quantum technology is considered to be quantum machine learning. We believe that the quantum classifier for SVMs using quantum kernels is one of the areas where quantum advantages can be demonstrated. At present, there are few examples of quantum classifiers applied to real problems in manufacturing processes. In this study, we aim to build a classifier that can demonstrate the quantum advantage and compare SVMs using classical and quantum kernels with conventional ResNet. Initially, a binarised image was generated after image pre-processing. After principal component analysis and dimensionality reduction were performed on the images, SVM with kernels was carried out. The kernel-based SVMs was then compared with the conventionally implemented Residual neural network (ResNet) using an evaluation index: F1-score. The results showed that the F1-score of SVMs using classical kernels was equivalent to that of Resnet. In addition, SVMs using quantum kernels showed higher F1-score than ResNet. In addition, the impact of the feature map and principal components of the quantum kernel was also investigated. It was found that when the feature map became more complex, conversely, circuit generation took more time. It was also found that the principal components are highly relevant to the image and cannot lead to simple results. In the future, we plan to accumulate more experimental data, look for scenes where quantum machine learning can be used and apply it to the manufacturing field.
Hybrid quantum-classical machine learning offers a promising direction for advancing automated quality control in industrial settings. In this study, we investigate two hybrid quantum-classical approaches for classifying defects in aluminium TIG welding images and benchmarking their performance against a conventional deep learning model. A convolutional neural network is used to extract compact and informative feature vectors from weld images, effectively reducing the higher-dimensional pixel space to a lower-dimensional feature space. Our first quantum approach encodes these features into quantum states using a parameterized quantum feature map composed of rotation and entangling gates. We compute a quantum kernel matrix from the inner products of these states, defining a linear system in a higher-dimensional Hilbert space corresponding to the support vector machine (SVM) optimization problem and solving it using a Variational Quantum Linear Solver (VQLS). We also examine the effect of the quantum kernel condition number on classification performance. In our second method, we apply angle encoding to the extracted features in a variational quantum circuit and use a classical optimizer for model training. Both quantum models are tested on binary and multiclass classification tasks and the performance is compared with the classical CNN model. Our results show that while the CNN model demonstrates robust performance, hybrid quantum-classical models perform competitively. This highlights the potential of hybrid quantum-classical approaches for near-term real-world applications in industrial defect detection and quality assurance.
Hybrid quantum–classical machine learning offers a promising direction for advancing automated quality control in industrial settings. In this study, we investigate two hybrid quantum–classical approaches for classifying defects in aluminum TIG welding images and benchmarking their performance against a conventional deep learning model. A convolutional neural network is used to extract compact and informative feature vectors from weld images, effectively reducing the higher-dimensional pixel space to a lower-dimensional feature space. Our first quantum approach encodes these features into quantum states using a parameterized quantum feature map composed of rotation and entangling gates. We compute a quantum kernel matrix from the inner products of these states, defining a linear system in a higher-dimensional Hilbert space corresponding to the support vector machine (SVM) optimization problem and solving it using a Variational Quantum Linear Solver (VQLS). We also examine the effect of the quantum kernel condition number on classification performance. In our second method, we apply angle encoding to the extracted features in a variational quantum circuit and use a classical optimizer for model training. Both quantum models are tested on binary and multiclass classification tasks, and the performance is compared with the classical CNN model. Our results show that while the CNN model demonstrates robust performance, hybrid quantum–classical models perform competitively. This highlights the potential of hybrid quantum–classical approaches for near-term real-world applications in industrial defect detection and quality assurance.
This thesis presents a systematic empirical evaluation of quantum machine learning performance under noisy intermediate-scale quantum (NISQ) era constraints. Through 670 controlled experiments, it evaluated quantum kernel support vector machines and variational quantum classifiers against classical baselines on MNIST binary and multiclass classification tasks with systematic variation of problem difficulty, feature dimensionality (4, 8 qubits), and training set size (n ∈{100, 250, 400, 500, 2000, 4000}). Statistical rigor was ensured through five random seeds per condition and comprehensive significance testing. During the testing with binary datasets, classical methods (SVM, logistic regression, k-NN, neural networks) achieved 85.9% to 99.6% accuracy with training times under one second across all sample sizes. Quantum kernel methods utilizing ZZ feature maps yielded 70.5% to 96.0% accuracy, requiring 129 to 2,140 seconds training with quadratic sample size scaling. Larger scale quantum experiments (n = 4000 for both architectures, 8-qubit encoding with n = 2000 for quantum kernels) proved computationally impossible on available hardware leading to out of memory errors, highlighting practical NISQ-era scalability constraints. Variational quantum classifiers exhibited persistent performance stagnation, failing to exceed 66.5% accuracy despite increases in training data and feature dimensionality. Performance gaps ranged from 3.3 to 35.2 percentage points. All quantum classical differences achieved statistical significance (p< 0.05, with 13 of 14 comparisons meeting p< 0.01) and exhibited large to very large efect sizes (d> 1.0, with 13 of 14 comparisons exceeding d> 2.0). To further evaluate performance and scalability, a 4 class classification experiment was conducted following an identical experimental protocol, with training set sizes of n ∈{100, 250, 400}. Across 180 runs, classical baselines achieved mean accuracies between 87.2% and 95% across both 4 and 8 dimensional data. Quantum Kernel SVM struggled during runs with 4 dimensions, accuracy varying between 64.6% and 74%. Quantum Kernel SVM accuracy declined 16%-25% when the dimension and qubit count were increased to 8, showing findings of exponential kernel concentration. The Variational Quantum Classifier also performed poorly, with accuracy ranging from 43.4% to 44.6% across all runs, providing further evidence that it failed to leverage additional features or training data. Experiments employed noiseless statevector simulation, establishing that quantum underperformance stems from fundamental algorithmic limitations independent of hardware noise. A further extension to 8 class classification (digits 0–7) confirmed and amplified these findings: classical methods maintained 65–83% accuracy while Quantum Kernel SVM degraded to 34–47% and VQC collapsed to 21–23%, approaching the 12.5% random baseline. Supplementary appendix experiments further examined amplitude encoding on raw 784 dimensional pixel features and an alternative angle encoding scheme on the 8-class task; the latter demonstrated that replacing the ZZ Feature Map with simple per qubit rotations substantially recovered quantum kernel performance, suggesting feature map design as a more consequential variable than circuit depth or qubit count in near-term quantum classification.
The paradigm of classical machine learning has been greatly revolutionized by the integration of the quantum computing paradigm, which has made it possible to solve complicated issues that were previously unsolvable by conventional computers. One such problem is accurately classifying cyclones with minimal data samples and feature parameters. In this paper, we investigate the superior learning capabilities of the Instantaneous Quantum Polynomial Embedding-based kernel Support Vector Machine classifier when compared to the classical Support Vector Machine (SVM) classifier, especially in scenarios with less sample size and feature vector size. Surprisingly, our outcomes reveal that the quantum kernel strategy outperforms its traditional counterpart, even with a smaller sample-based dataset. This incredible performance can be attributed to the quantum core strategy’s exceptional ability to extract complex attributes, which enables it to extract more relevant insights from a smaller number of data points. We present a Quantum Kernel Support Vector Machine (QKSVM) built as a Quantum Kernel Circuit (QKC) in three configurations with varied qubit counts utilizing distinct embedding techniques: Angle embedding (QKSVM Angle), Instantaneous Quantum Polynomial embedding (QKSVM IQP), and Quantum Approximation Optimization Algorithm (QKSVM QAOA) embedding. Our results demonstrate that the QKSVM approach surpasses traditional SVM, especially when IQP embedding is employed. The QSVM model trained with four Principal Component Analysis (PCA) based selected relevant features outperforms the other QKC variations in cyclone classification, obtaining an excellent test accuracy rate of 91.66%.