Abstract: This study addresses scheduling challenges arising from the integration of superconducting quantum processors into heterogeneous computing environments.It introduces a unified modeling framework for hybrid task graphs and proposes a joint scheduling strategy that minimizes communication overhead while leveraging predictive allocation of quantum execution windows.The proposed method is evaluated in terms of its potential to reduce execution delays and improve overall resource utilization.Further analysis explores the model's robustness and scalability under computationally intensive scenarios,along with the adaptability of task partitioning approaches to varying system constraints.The findings contribute to practical methodologies for ena-bling quantum-classical hybrid computing and advancing intelligent resource coordination in next-generation high-performance systems.

Key words: Superconducting quantum processor, Heterogeneous computing, Collaborative scheduling, Task partitioning, Parallel computing