High Performance Computing-Quantum Computing (HPCQC) platforms expose multiple Quantum Processing Units (QPUs) that may differ in size, topology, native gates, and noise characteristics. For current noisy devices, errors compound along the compiled circuits quickly, and minimizing them, that is, maximizing the circuits'execution fidelity, is essential for reliable results. Fidelity depends on the compilation to a specific target device: the same high-level circuit may produce different executables and, therefore, different expected fidelities across QPUs. We present a low-overhead fidelity-aware scheduling framework for multi-QPU systems based on a Graph Neural Network (GNN) that estimates, before compilation, the expected fidelity of each circuit on each available QPU. Then, a tunable scheduler uses these estimates to control the trade-off between execution fidelity and parallelism. Results show that this framework allows for approximating an exhaustive fidelity-based assignment, saving computational resources compared to a brute-force approach that compiles each circuit on every device.
Innocenzo Fulginiti, Antonio Tudisco, Salvatore Zammuto et al.· 0 citations
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.
Antonio Tudisco, Patrick Hopf, Linus Schulte et al.· 2026 IEEE International Conf...· 2 citations
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