Quantum kernel methods encode classical data into quantum states using specially designed feature-map circuits and then train a classical SVM on the resulting kernel matrix. Prior studies of entanglement structure typically compare only a few basic topologies, such as linear, circular, and full entanglement, leaving the broader space of entanglement graphs largely unexplored. This study exhaustively evaluates all possible entanglement topologies of a TwoLocal feature map and benchmarks them against classical methods and standard quantum feature maps on two synthetic datasets (Adhoc3_150 and Adhoc4_150) and two real-world datasets (Blood and Banknote). On Adhoc4_150, the best TwoLocal topology achieves an accuracy of 0.7556, matching Pauli Z; on Blood, it reaches 0.6889, exceeding RBF SVM and Pauli Z at 0.6667; and on Banknote, it achieves 1.0000, compared with 0.9778 for RBF SVM. In contrast, Random Forest achieves the highest accuracy on Adhoc3_150 at 0.8222, exceeding all evaluated quantum configurations. Across all four datasets, increasing the number of entangling pairs does not consistently improve accuracy, while the standard linear, circular, and full Pauli ZZ topologies never outperform Pauli Z. To further characterize this behavior, we propose a factorial linear modeling approach that quantifies the contribution of individual entanglement edges and qubit connectivity to classification accuracy. On Adhoc4_150, two edges exhibit significant negative effects, whereas Blood and Banknote show both significant positive and negative edge effects. The q0q1 edge is significant across all three four-feature datasets but reverses direction across datasets. These results indicate that the effect of entanglement depends jointly on the feature-map architecture, dataset, and specific qubit connections rather than on entanglement density alone. The proposed framework can also be applied to sampled topology sets, providing a basis for targeted entanglement analysis beyond exhaustively enumerable low-qubit systems.
Quantum kernel methods encode classical data into quantum states using specially designed feature-map circuits and then train a classical SVM on the resulting kernel matrix. Prior studies of entanglement structure typically compare only a few basic topologies, such as linear, circular, and full entanglement, leaving the broader space of entanglement graphs largely unexplored. This study exhaustively evaluates all possible entanglement topologies of a TwoLocal feature map and benchmarks them against classical methods and standard quantum feature maps on two synthetic datasets (Adhoc3_150 and Adhoc4_150) and two real-world datasets (Blood and Banknote). On Adhoc4_150, the best TwoLocal topology achieves an accuracy of 0.7556, matching Pauli Z; on Blood, it reaches 0.6889, exceeding RBF SVM and Pauli Z at 0.6667; and on Banknote, it achieves 1.0000, compared with 0.9778 for RBF SVM. In contrast, Random Forest achieves the highest accuracy on Adhoc3_150 at 0.8222, exceeding all evaluated quantum configurations. Across all four datasets, increasing the number of entangling pairs does not consistently improve accuracy, while the standard linear, circular, and full Pauli ZZ topologies never outperform Pauli Z. To further characterize this behavior, we propose a factorial linear modeling approach that quantifies the contribution of individual entanglement edges and qubit connectivity to classification accuracy. On Adhoc4_150, two edges exhibit significant negative effects, whereas Blood and Banknote show both significant positive and negative edge effects. The q0q1 edge is significant across all three four-feature datasets but reverses direction across datasets. These results indicate that the effect of entanglement depends jointly on the feature-map architecture, dataset, and specific qubit connections rather than on entanglement density alone. The proposed framework can also be applied to sampled topology sets, providing a basis for targeted entanglement analysis beyond exhaustively enumerable low-qubit systems.
This study presents a comparative empirical evaluation of Variational Quantum Classifier (VQC) performance within the Quantum Machine Learning (QML) framework, focusing on VQC architectural analysis under NISQ-era qubit constraints. The VQC was designed utilizing ZFeatureMap for data encoding and EfficientSU2 as the ansatz. A systematic test was performed to evaluate the impact of three entanglement topologies (default, circular, pairwise) and two qubit scales (4 and 8) with four classical optimizers on standard classification datasets: Iris, Wine, and Breast Cancer. The results demonstrated that no single configuration is universally optimal. The default topology achieved the highest peak accuracy on multi-class datasets (Iris: 97% with P-BFGS), suggesting its efficiency in simpler parameter landscapes. Conversely, the pairwise and circular topologies showed superior stability and competitive accuracy (up to 92%) on the high-dimensional Breast Cancer dataset, confirming that richer qubit connectivity is essential for effective feature separation in more complex classification problems. Increasing the scale from 4 to 8 qubits was found to be crucial for improving the overall stability and consistency of performance rather than merely increasing peak accuracy. These findings provide essential empirical guidance for designing optimal and trainable VQC architectures under current quantum device limitations.
Parameterized quantum circuits (PQCs) provide a flexible substrate for hybrid quantum machine learning (QML), but their practical value on Noisy Intermediate-Scale Quantum (NISQ) devices remains an empirical question, especially because training depth and scale can introduce optimization challenges such as barren plateaus. Here we study how the number and topology of two-qubit entangling gates in the feature-map stage influence a fixed hybrid QNN workflow for classifying strong versus weak epitope-receptor binding in Porcine Reproductive and Respiratory Syndrome (PRRS) vaccine design. The dataset consists of docking-derived binding affinities for N=80 9-mer epitopes, labeled as Strong or Weak binding, and partitioned into training, validation, and test subsets using a 40:30:30 split. We compare a classical CNN benchmark with a hybrid Embedding-QNN architecture under four feature-map configurations: a non-entangling Z feature map, an all-to-all high-entanglement ZZ feature map, and two interleaved nearest-neighbour entanglement patterns of low and high depth. Among the configurations tested, the high-entanglement ZZ feature map is seen to provide the strongest evidence of reduced training-set overfit, with a lower training area under the accuracy curve (AUAC) and the highest test/training AUAC ratio, while preserving competitive test-set accuracy. These results do not establish a general QML advantage, but they suggest that feature-map entanglement topology is a meaningful design variable for sparse biological screening tasks and warrants further evaluation with additional metrics, larger datasets, and noise-aware or hardware-based experiments.
Quantum kernel methods are often proposed as a route to improved medical image classification because quantum feature maps can embed data into high-dimensional Hilbert spaces. However, their practical value remains unclear when compared with strong classical kernels under leakage-controlled conditions. This study benchmarks two simulated quantum kernel support vector machines, QSVM-ZZ and QSVM-Pauli, against logistic regression, linear SVM, fixed-default RBF-SVM, and a compact multi-layer perceptron on BrainTumorMRI and BreastMNIST. All models used identical frozen ResNet18 features, train-only standardisation, PCA compression to four and eight dimensions, three random seeds, and matched classifier-training budgets where applicable. Across 24 seed-level matched comparisons, the best quantum kernel model never outperformed the best classical model. The mean quantum-minus-classical macro-F1 gap was - 0.2230, with all 24 gaps negative. Fixed pairwise comparisons confirmed that RBF-SVM exceeded both quantum kernels individually. QSVMs also required approximately 50 × to more than 220 × higher runtime. These findings show that, for compressed ResNet18 medical image features under noiseless simulation, the tested QSVM-ZZ and QSVM-Pauli kernels did not provide a practical advantage over fixed-default RBF-SVM.
Abstract Heart disease remains to be a primary cause of death globally, requiring precise and timely predictive methodologies. Traditional machine learning methods that depend exclusively on classical clinical variables frequently have restricted predictive accuracy and do not adequately use the intricate nonlinear interactions present in patient data. This work examines the effectiveness of hybrid quantum-classical models for predicting heart disease by combining classical clinical data with quantum-derived representations via Variational Quantum Classifier (VQC), Estimator, and Sampler models. Twelve hybrid configurations, integrating three quantum feature maps (Z, ZZ, Pauli) with four ansatz architectures (RealAmplitudes, TwoLocal, EfficientSU2, PauliTwoDesign), were assessed inside the PennyLane and Qiskit frameworks. The results indicate that the VQC model in PennyLane reached optimal performance with Z–TwoLocal and ZZ–PauliTwoDesign configurations, achieving accuracy and F1-scores of up to 0.95. The Estimator model consistently produced reliable predictions, with Pauli–EfficientSU2 and Z–TwoLocal attaining accuracies of up to 0.91 and F1-scores of 0.92 across several frameworks. The Sampler model demonstrated 1reasonable performance, with the Pauli–EfficientSU2 configuration achieving an accuracy of 0.65 and an F1-score of 0.73, underscoring the supplementary role of stochastic quantum sampling. The analysis of feature significance indicated that the quantum-derived feature provided the most robust predictive signal, exceeding classical variables like Age, Blood Pressure, and Cholesterol. These findings demonstrate that meticulous alignment of feature maps and ansatz circuits is essential for optimizing model expressivity, convergence, and predictive effectiveness. In summary, hybrid quantum-classical models, especially the Estimator and VQC architectures, significantly improve heart disease prediction, providing a feasible method for precision clinical decision support and potential incorporation into privacy-preserving, multi-institutional healthcare systems.
This work presents a quantum machine learning approach for bipartite entanglement detection in 3×3 quantum systems using a support vector machine with quantum kernels. The classifier is trained to distinguish separable from entangled states, which includes both PPT-detectable and PPT-non-detectable instances, based on classical simulations of parameterized quantum feature maps combined with dimensionality reduction via PCA. Our results show that Pauli-based feature map kernels achieve higher classification accuracy than amplitude embedding at the same PCA dimension, reaching up to 78% accuracy for PCA =8, while amplitude embedding requires PCA =32 to attain comparable performance. We also show that increasing the number of Pauli feature map repetitions does not lead to consistent accuracy improvements, despite significantly increasing circuit depth and computational overhead.
Parity (XOR) classification requires detecting discrete, high-order feature interactions that smooth classical kernels cannot efficiently capture. We study how quantum kernel advantage depends on parity complexity, the number of features entering the XOR rule, and find a clear threshold behavior. We pair a ZZ quantum feature map with binary {0, pi} encoding (features median thresholded before circuit input) to expose parity structure. A binary encoding ablation, RBF SVM trained on the identical {0, pi} features, separates encoding from circuit effects: at low complexity (n = 5 features), binary RBF achieves 83.4% +/- 1.7% and the quantum kernel 81.2% +/- 1.9%, showing encoding drives performance there. At high complexity (n = 11 features, 11 qubits, r = 3 ZZ repetitions), all classical methods collapse to near-random (approx. 50%), binary RBF reaches only 54.3% +/- 1.1%, and the quantum ZZ kernel achieves 66.3% +/- 3.2% (mean +/- std, 10 seeds), a +12.0 percentage-point margin over the binary ablation and approx. 7x higher kernel-target alignment (0.094 +/- 0.020 vs. 0.013 +/- 0.001). These results identify parity complexity as a concrete axis along which genuine quantum kernel advantage, not attributable to encoding alone, emerges.