Adaptive job and resource management for the growing quantum cloud

TL;DR

This paper introduces an adaptive job scheduling approach for quantum cloud computing that predicts fidelity and queuing times to optimize resource management, significantly reducing wait times and enhancing fidelity.

Contribution

It presents a novel predictive scheduling framework for quantum clouds, incorporating fidelity and queuing time models to improve efficiency over traditional methods.

Findings

Reduced wait times by over 3x

Improved fidelity by over 40% in specific scenarios

Effective on simulated IBM quantum systems

Abstract

As the popularity of quantum computing continues to grow, efficient quantum machine access over the cloud is critical to both academic and industry researchers across the globe. And as cloud quantum computing demands increase exponentially, the analysis of resource consumption and execution characteristics are key to efficient management of jobs and resources at both the vendor-end as well as the client-end. While the analysis and optimization of job / resource consumption and management are popular in the classical HPC domain, it is severely lacking for more nascent technology like quantum computing. This paper proposes optimized adaptive job scheduling to the quantum cloud taking note of primary characteristics such as queuing times and fidelity trends across machines, as well as other characteristics such as quality of service guarantees and machine calibration constraints. Key components of the proposal include a) a prediction model which predicts fidelity trends across machine based on compiled circuit features such as circuit depth and different forms of errors, as well as b) queuing time prediction for each machine based on execution time estimations. Overall, this proposal is evaluated on simulated IBM machines across a diverse set of quantum applications and system loading scenarios, and is able to reduce wait times by over 3x and improve fidelity by over 40\% on specific usecases, when compared to traditional job schedulers.