Quantum Processing Unit (QPU) processing time Prediction with Machine Learning

TL;DR

This paper demonstrates how machine learning models, specifically Gradient-Boosting algorithms, can accurately predict quantum processing times, thereby improving resource management and operational efficiency in quantum computing systems.

Contribution

It introduces ML-based predictive models for QPU processing times using a large dataset, enhancing quantum job scheduling and resource allocation.

Findings

ML models accurately predict quantum processing times

Data preprocessing improves model accuracy

Potential to optimize quantum computing resource management

Abstract

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.