  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.
