Learning to Build Quantum Kernels: A Reinforcement Learning Framework for Quantum SVC Optimization

Abstract This work introduces a novel hybrid quantum-classical machine learning framework in which a Reinforcement Learning (RL) agent autonomously constructs quantum circuits to optimize kernels for a Quantum Support Vector Classifier (QSVC). Unlike traditional approaches that rely on fixed or manually designed quantum feature maps, the proposed method employs a Deep Q-Network (DQN) agent to dynamically select quantum gates and their parameters. These configurations define a parameterized quantum circuit whose fidelity-based kernel is used to perform classification tasks. The RL agent is trained to maximize classification accuracy by interacting with an environment that evaluates circuit performance. Experiments conducted on a realistic and potentially biased educational dataset demonstrate that the RL-optimized QSVC outperforms both classical SVC and standard QSVC baselines in terms of accuracy and fairness. Furthermore, SHAP-based explainability analysis reveals a reduction in model bias, suggesting improved interpretability and robustness. This study highlights the potential of reinforcement learning for automated quantum circuit design and kernel optimization in quantum machine learning.