Quantum Kernel Methods: Convergence Theory, Separation Bounds and Applications to Marketing Analytics
This work provides an incremental step for marketing analytics by demonstrating feasibility of quantum methods on real data in noisy quantum hardware.
The paper tackled applying quantum kernel methods to a consumer classification task in the NISQ regime, achieving 0.7790 accuracy and 0.8100 F1 score with a hybrid quantum-classical pipeline, showing higher sensitivity than classical SVM.
This work studies the feasibility of applying quantum kernel methods to a real consumer classification task in the NISQ regime. We present a hybrid pipeline that combines a quantum-kernel Support Vector Machine (Q-SVM) with a quantum feature extraction module (QFE), and benchmark it against classical and quantum baselines in simulation and with limited shallow-depth hardware runs. With fixed hyperparameters, the proposed Q-SVM attains 0.7790 accuracy, 0.7647 precision, 0.8609 recall, 0.8100 F1, and 0.83 ROC AUC, exhibiting higher sensitivity while maintaining competitive precision relative to classical SVM. We interpret these results as an initial indicator and a concrete starting point for NISQ-era workflows and hardware integration, rather than a definitive benchmark. Methodologically, our design aligns with recent work that formalizes quantum-classical separations and verifies resources via XEB-style approaches, motivating shallow yet expressive quantum embeddings to achieve robust separability despite hardware noise constraints.