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Title: Adaptive Circuit Learning for Quantum Metrology
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 more » times by over 3x and improve fidelity by over 40% on specific usecases, when compared to traditional job schedulers. « less
Authors:
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Award ID(s):
1730449 2016136 1818914
Publication Date:
NSF-PAR ID:
10313467
Journal Name:
2021 IEEE International Conference on Quantum Computing and Engineering (QCE)
Sponsoring Org:
National Science Foundation
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