Abstract This work presents an empirical study of a Quantum Support Vector Machine(QSVM) for classification of radar micro-Doppler signatures, used here as astructured benchmark for evaluating quantum kernel methods under realistichardware constraints. Extracted classical features are reduced using PrincipalComponent Analysis (PCA) to enable compact quantum encoding, and mappedinto a quantum kernel feature space through a fully entangling ZZFeatureMap.Classification is performed using a kernel-based QSVM and compared with aclassical SVM baseline under consistent preprocessing and evaluation conditions.Performance is assessed in both ideal simulation and on NISQ-era superconducting quantum processors, specifically IBM Torino (133-qubit) and IBM Fez(156-qubit). Results show that the QSVM attains competitive accuracy whileoperating on reduced feature dimensionality, with performance sensitivity tonoise, decoherence, and measurement shot count. Improved kernel stability isobserved on newer hardware architecture.The study does not claim quantumadvantage; instead, it provides a hardware-level evaluation of quantum kernelbehavior and clarifies current feasibility and limitations of QSVM approaches forstructured signal classification tasks.
Abstract This work presents an empirical study of a Quantum Support Vector Machine(QSVM) for classification of radar micro-Doppler signatures, used here as astructured benchmark for evaluating quantum kernel methods under realistichardware constraints. Extracted classical features are reduced using PrincipalComponent Analysis (PCA) to enable compact quantum encoding, and mappedinto a quantum kernel feature space through a fully entangling ZZFeatureMap.Classification is performed using a kernel-based QSVM and compared with aclassical SVM baseline under consistent preprocessing and evaluation conditions.Performance is assessed in both ideal simulation and on NISQ-era superconducting quantum processors, specifically IBM Torino (133-qubit) and IBM Fez(156-qubit). Results show that the QSVM attains competitive accuracy whileoperating on reduced feature dimensionality, with performance sensitivity tonoise, decoherence, and measurement shot count. Improved kernel stability isobserved on newer hardware architecture.The study does not claim quantumadvantage; instead, it provides a hardware-level evaluation of quantum kernelbehavior and clarifies current feasibility and limitations of QSVM approaches forstructured signal classification tasks.
This paper examines the application of a Quantum Support Vector Machine (QSVM) for radarbased aerial target classification using micro-Doppler signatures. Classical features are extracted and reduced via Principal Component Analysis (PCA) to enable efficient quantum encoding. The reduced feature vectors are embedded into a quantum kernel-induced feature space using a fully entangled ZZFeatureMap and classified using a kernel based QSVM. Performance is first evaluated on a quantum simulator and subsequently validated on NISQ-era superconducting quantum hardware, specifically the IBM Torino (133-qubit) and IBM Fez (156-qubit) processors. Experimental results demonstrate that the QSVM achieves competitive classification performance relative to classical SVM baselines while operating on substantially reduced feature dimensionality. Hardware experiments reveal the impact of noise and decoherence and measurement shot count on quantum kernel estimation, and further show improved stability and fidelity on newer Heron r2 architecture. This study provides a systematic comparison between simulator-based and hardware-based QSVM implementations and highlights both the feasibility and current limitations of deploying quantum kernel methods for practical radar signal classification tasks.
The rapid advancement of Noisy Intermediate-Scale Quantum (NISQ) devices necessitates a systematic evaluation of quantum machine learning (QML) algorithms to determine their feasibility for near-term applications. This study aims to benchmark the Variational Quantum Classifier (VQC)and the Quantum Kernel Support Vector Machine (QSVM) under realistic noise conditions modeled using Qiskit Aer. The primary objective is to investigate the impact of depolarizing and amplitude damping noise on the classification performance of these algorithms. Using the Iris binary classification task, we simulated four noise levels (0, 0.01, 0.05, 0.1) and evaluated accuracy across 1024 measurement shots. Results show that QSVM achieves superior accuracy (70%) under ideal and lownoise conditions, while VQC exhibits higher resilience at moderate noise (50% at 0.05 noise level) due to its adaptive Variational parameters. At higher noise levels (0.1), both models converge to ~35–40% accuracy, indicating significant degradation. These findings provide actionable insights for algorithm selection on NISQ hardware, with VQC favored for moderately noisy devices and QSVM preferable for low-noise or error-mitigated systems. The study concludes by highlighting the need for noise-aware ansatz design and error mitigation techniques to enhance QML performance in NISQ regimes.
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.
Abstract Support vector machines (SVMs) are widely used for classification, but become computationally expensive as datasets grow and data become high-dimensional or nonlinearly separable. Quantum support vector machines (QSVMs) enhance the traditional SVMs by replacing conventional feature mappings with quantum-implemented embeddings that project data into high-dimensional Hilbert spaces, potentially improving expressive power and efficiency. However, training QSVMs on current noisy intermediate-scale quantum (NISQ) devices remains challenging, as constructing the quantum kernel matrix requires repeated circuit executions and scales quadratically with dataset size. We propose NQ-SVM , a hybrid quantum-classical method that incorporates the Nystr\"om approximation to enhance the scalability of QSVMs. By selecting a small set of landmark points, the Nystr\"om method yields a low-rank kernel approximation, reducing complexity from (O(n2)) to (O(nm)), while preserving the quantum advantages in handling high-dimensional, non-linear data. Extensive simulations demonstrate that NQ-SVM achieves comparable classification accuracy to classical SVMs and full QSVMs, while substantially reducing training time and kernel evaluation costs. These results underscore the potential of NQ-SVM as a scalable and practical hybrid framework for near-term quantum machine learning (QML).
Abstract Support vector machine(SVM) is one of the most popular machine learning(ML) methods which are widely used as the methodology of choice in Breast Cancer detection because of its unique advantages in critical features detection from complex breast cancer datasets. Quantum support vector machine(QSVM) uses the power of quantum mechanics to improve the performance of classical support vector machine(SVM) algorithms with theoretical acceleration advantage. However, it still suffers from the wide error problem and hardware limits in Noisy Intermediate-Scale Quantum computing(NISQ). Consequently, we propose a quantum kernel estimation method with measurement error mitigation and test it using the Wisconsin Breast Cancer database first on the IBM quantum processors. The experimental results show that we can achieve remarkable performance improvement in accuracy for solving such binary classification problems compared to state-of-the-art models, which shows the great potential for the design and implementation of machine learning algorithms with quantum advantages in the future.
Detecting credit card fraud is notoriously difficult because fraudulent charges make up less than 0.2% of total transactions. Quantum Machine Learning (QML), particularly Quantum Kernel Support Vector Machines (QSVM), promises richer feature representations by embedding classical data into high-dimensional Hilbert spaces. However, whether this theoretical advantage holds up when managing real-world financial data bottlenecks remains unclear. We designed a controlled benchmark comparing three quantum encodings, Angle, ZZFeatureMap, and IQP against classical models while keeping sample sizes, feature counts, and scaling strictly matched. To handle class skew without corrupting quantum state overlaps, we embedded synthetic minority oversampling (SMOTE) directly into the quantum Gram matrix workflow, raising PR-AUC scores by roughly +0.04 across feature maps. When trained on the full European credit card dataset with 30 features, classical baselines reached a top PR-AUC of 0.983. However, restricting classical models to match QSVM hardware constraints (500 samples, 4 PCA features) dropped their PR-AUC to 0.750. This puts our best quantum result 0.730 PR-AUC using IQP encoding on 150 samples into realistic context. Testing on physical quantum hardware reduced performance further to 0.62 due to CNOT gate errors and thermal noise. Overall, our findings pinpoint two main operational bottlenecks for near-term quantum kernels: the scaling of Gram matrix evaluation and heavy information loss from squeezing features onto small qubit registers.