Adaptive job and resource management for the growing quantum cloud

Conference · Fri Nov 19 04:00:00 EST 2021

DOI:https://doi.org/10.1109/qce52317.2021.00047· OSTI ID:1865307

Ravi, Gokul Subramanian ^[1]; Smith, Kaitlin N. ^[2]; Murali, Prakash ^[3]; Chong, Frederic T. ^[2]

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1. Univ. of Chicago, IL (United States); The University of Chicago
2. Univ. of Chicago, IL (United States)
3. Princeton Univ., NJ (United States)

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. Altogether, 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.

• Research Organization: Univ. of Chicago, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Engineering & Technology; National Science Foundation; EPiQC
• DOE Contract Number: SC0020289; SC0020331
• OSTI ID: 1865307
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Fidelity
Job scheduling
Quantum computing
Quantum machine
Queues
Resource management