Fidelity-Aware Scheduling of Quantum Circuits on Multi-QPU Systems

2026-09-09

AI Summary

研究提出了一种基于图神经网络的保真度感知调度框架，用于多量子处理单元系统中，以平衡电路执行保真度和并行性。

Abstract

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.

Problem

• Quantum Processing Units
• Fidelity
• Scheduling
• Graph Neural Network
• Compilation

Innovation

• fidelity-aware scheduling
• Graph Neural Network (GNN)
• multi-QPU systems
• execution fidelity