A hybrid framework based on quantum genetic algorithm and quantum support vector machine for noise-aware qubit mapping in NISQ systems

Quantum circuit compilation and qubit mapping represent critical challenges in noisy intermediate-scale quantum (NISQ) devices due to limited qubit connectivity and the presence of hardware noise. Conventional heuristic mapping methods often generate suboptimal solutions under realistic noisy execution conditions. This paper proposes a hybrid learning-guided framework that integrates a quantum genetic algorithm (QGA) with a quantum support vector machine (QSVM) for noise-aware qubit mapping optimization in NISQ systems. In the proposed framework, QGA generates diverse candidate mappings, while QSVM-based supervised ranking identifies high-quality mappings using structural and execution-related features. To evaluate the effectiveness of the proposed mapping selection strategy, a QROC/Q-AUC-based evaluation mechanism is introduced to quantify the decision reliability and ranking capability of the mapping selection process under noisy quantum execution conditions. Extensive experiments on benchmark quantum circuits using noisy simulation environments derived from IBM Quantum hardware models demonstrate that the proposed framework reduces SWAP overhead and circuit depth while improving execution fidelity and the reliability of selecting high-quality mappings compared with conventional heuristic and evolutionary approaches. The results indicate that the integration of evolutionary optimization and quantum machine learning provides an effective solution for noise-aware qubit mapping in NISQ-era quantum compilation.