aiqbench analysis

This research introduces a Graph Neural Network-based framework that predicts quantum circuit fidelity across heterogeneous processors before compilation, significantly reducing the computational overhead of traditional exhaustive testing methods. By intelligently distributing workloads based on estimated error rates, the system optimizes resource usage and accelerates task scheduling on noisy intermediate-scale quantum devices. This advancement directly addresses the critical challenge of scaling multiprocessor quantum computing architectures through efficient, AI-driven workload management.