Quantum Processing Unit (QPU) processing time Prediction with Machine Learning
This work addresses resource management and scheduling inefficiencies in quantum computing systems, but it is incremental as it applies existing ML methods to a new domain.
This paper tackled the problem of predicting Quantum Processing Unit (QPU) processing times for quantum jobs using machine learning, achieving effective forecasting with a dataset of about 150,000 IBM Quantum jobs.
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.