用机器学习预测量子任务运行时间，提升系统调度效率
Quantum Processing Unit (QPU) processing time Prediction with Machine Learning
- 基于梯度提升算法构建预测模型，处理15万条量子任务数据
- 模型准确率显著提升，助力资源管理与任务调度优化
- 适合量子计算运维与算法调度研究者参考
本文探索了机器学习（ML）技术在预测量子处理器（QPU）任务执行时间中的应用。基于约15万条遵循IBM Quantum架构的量子任务数据，采用梯度提升方法（LightGBM）构建预测模型，并结合数据预处理提升精度。实验结果表明，该方法能有效预测量子任务的QPU处理时间，有助于改进量子计算系统的资源管理与任务调度。研究不仅展示了机器学习在量子任务预测中的潜力，也为未来引入AI驱动工具支持先进量子运算奠定了基础。
原文摘要 · Abstract (English)
This paper explores the application of machine learning (ML) techniques in predicting the QPU processing time of quantum jobs. By leveraging ML algorithms, this study introduces predictive models that are designed to enhance operational efficiency in quantum computing systems. Using a dataset of about 150,000 jobs that follow the IBM Quantum schema, we employ ML methods based on Gradient-Boosting (LightGBM) to predict the QPU processing times, incorporating data preprocessing methods to improve model accuracy. The results demonstrate the effectiveness of ML in forecasting quantum jobs. This improvement can have implications on improving resource management and scheduling within quantum computing frameworks. This research not only highlights the potential of ML in refining quantum job predictions but also sets a foundation for integrating AI-driven tools in advanced quantum computing operations.
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