Fidelity-Based Quantum Device Selection Using Graph Neural Networks
Modern High-Performance Computing (HPC) infrastructures are increasingly integrating multiple Quantum Processing Units (QPUs) of different types alongside classical resources. Unlike traditional accelerator scheduling, quantum device selection affects not only performance but also the quality of the computed result because current quantum hardware exhibits device-dependent noise characteristics. This makes efficient and fidelity-based device selection a key challenge in multi-QPU environments. In this work, we propose a Graph Neural Network (GNN) model that exploits the structural representation of quantum circuits to predict their execution fidelity across different QPUs. By operating on uncompiled circuits, the approach supports earlystage device selection and enables making informed scheduling decisions. We evaluate the model on a diverse set of benchmark circuits compiled for superconducting and trapped-ion devices. The results show that the proposed approach outperforms existing baseline models, achieving 30% Mean Squared Error (MSE) and 20% Mean Absolute Error (MAE) reduction, and provides an effective basis for fidelity-based scheduling in heterogeneous HPC-quantum systems.