{"total":22,"rows":[{"id":"2503.05602","title":"On the similarity of bandwidth-tuned quantum kernels and classical kernels","created":"2025-03-07","cited_by_count":3,"area":null},{"id":"2508.00768","title":"Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy","created":"2025-08-01","cited_by_count":3,"area":null},{"id":"2605.14672","title":"AQKA: Active Quantum Kernel Acquisition Under a Shot Budget","created":"2026-05-14","cited_by_count":3,"area":null},{"id":"2604.07896","title":"Non-variational supervised quantum kernel methods: a review","created":"2026-04-09","cited_by_count":2,"area":null},{"id":"2604.18837","title":"Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation","created":"2026-04-20","cited_by_count":2,"area":null},{"id":"2507.12117","title":"Quantum Machine Learning in Multi-Qubit Phase-Space Part I: Foundations","created":"2025-07-16","cited_by_count":1,"area":null},{"id":"2602.00048","title":"Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning","created":"2026-01-19","cited_by_count":1,"area":null},{"id":"2602.18377","title":"Theory and interpretability of Quantum Extreme Learning Machines: a Pauli-transfer matrix approach","created":"2026-02-20","cited_by_count":1,"area":null},{"id":"2605.06035","title":"Quantum Kernels for Audio Deepfake Detection Using Spectrogram Patch Features","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2502.21025","title":"AutoQML: A Framework for Automated Quantum Machine Learning","created":"2025-02-28","cited_by_count":0,"area":null},{"id":"2504.14002","title":"Predicting fermionic densities using a Projected Quantum Kernel method","created":"2025-04-18","cited_by_count":0,"area":null},{"id":"2505.24765","title":"Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2510.11744","title":"Quantum Kernel Methods: Convergence Theory, Separation Bounds and Applications to Marketing Analytics","created":"2025-10-11","cited_by_count":0,"area":null},{"id":"2512.11367","title":"Maritime object classification with SAR imagery using quantum kernel methods","created":"2025-12-12","cited_by_count":0,"area":null},{"id":"2602.06381","title":"HyQuRP: Hybrid quantum-classical neural network with rotational and permutational equivariance","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2602.19644","title":"Spectral Phase Encoding for Quantum Kernel Methods","created":"2026-02-23","cited_by_count":0,"area":null},{"id":"2606.14194","title":"Hybrid Classical-Quantum (HCQ) Alzheimer's Classification via Supervised $\\beta$-VAE and Quantum Kernels","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2607.13737","title":"Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2607.20377","title":"Statevector-to-Hardware Reconstruction of a Four-Qubit ZZ Quantum Kernel: A Single-Backend Case Study of Three Execution Jobs","created":"2026-07-22","cited_by_count":0,"area":null},{"id":"2608.06846","title":"Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification","created":"2026-08-07","cited_by_count":0,"area":null},{"id":"2609.00475","title":"Fractal dimension predicts quantum kernel collapse in angle-encoded data","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.04388","title":"Candidate Comparability Before Promotion: Conditional Validation in Adaptive Network Intrusion Detection","created":"2026-09-03","cited_by_count":0,"area":null}],"next_cursor":null}