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US 12,743,307 B2Granted
Quantum job submission and optimization for end-to-end algorithms with prioritization of placeholders of quantum jobs in job queue
AssigneeDell Products L.P.
InventorsKenneth Durazzo, Stephen J. Todd, Michael Robillard, Victor Fong, Eric Bruno, Benjamin Santaus
Life of this patent
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Abstract
Quantum job prioritization is disclosed. Quantum jobs may be stored as placeholders in a job queue associated with a quantum processing unit. The quantum jobs are prioritized to improve the usage of the quantum processing unit. Prioritizing quantum jobs allows the quantum processing unit to execute quantum jobs in different orders rather than on an application basis. This allows grace periods to be used for executing quantum jobs.
Claims20
A method comprising:
- accessing an orchestration engine that includes a prioritization engine and a machine learning model, wherein the machine learning model is tasked with predicting runtime characteristics for quantum jobs of an application in a job queue, and wherein the prioritization engine is tasked with evaluating the quantum jobs of the application and prioritizing the quantum jobs based on the runtime characteristics;
- placing the quantum jobs of the application in the job queue as placeholders, wherein the application includes a computing job, wherein at least some details of each of the quantum jobs are omitted in the respective placeholders, and wherein, when a time for executing one of the quantum jobs arrives, a classical system is notified to submit said one quantum job;
- inserting respective quantum job metadata into the respective placeholders, the respective quantum job metadata including metadata structured to enable the prioritization engine to prioritize the quantum jobs;
- causing the prioritization engine to prioritize the placeholders of the quantum jobs in the job queue based on a determined grace period associated with execution of the computer job of the application, wherein prioritizing the placeholders of the quantum jobs comprises applying the machine learning model, which is trained on different quantum job metadata inserted in the respective placeholders, to predict the runtime characteristics of each of the quantum jobs and using the prioritization engine to prioritize the placeholders of the quantum jobs based on the runtime characteristics, and wherein the grace period is dynamically determined as a time required by the computer job to generate an output that serves as input for a corresponding quantum job for execution in a quantum processing unit;
- in response to a determination that a highest priority quantum job associated with a different application from the job queue is to be executed, obtaining substance of the highest priority quantum job by notifying the classical system to submit the highest priority quantum job;
- after obtaining the substance of the highest priority quantum job, sending the highest priority quantum job to the quantum processing unit for execution;
- and
- executing the highest priority quantum job during the determined grace period of the computer job of the application.
5 more elements
A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:
- accessing an orchestration engine that includes a prioritization engine and a machine learning model, wherein the machine learning model is tasked with predicting runtime characteristics for quantum jobs of an application in a job queue, and wherein the prioritization engine is tasked with evaluating the quantum jobs of the application and prioritizing the quantum jobs based on the runtime characteristics;
- placing the quantum jobs of the application in the job queue as placeholders, wherein the application includes a computing job, wherein at least some details of each of the quantum jobs are omitted in the respective placeholders, and wherein, when a time for executing one of the quantum jobs arrives, a classical system is notified to submit said one quantum job;
- inserting respective quantum job metadata into the respective placeholders, the respective quantum job metadata including metadata structured to enable the prioritization engine to prioritize the quantum jobs;
- causing the prioritization engine to prioritize the placeholders of the quantum jobs in the job queue based on a determined grace period associated with execution of the computer job of the application, wherein prioritizing the placeholders of the quantum jobs comprises applying the machine learning model, which is trained on different quantum job metadata inserted in the respective placeholders, to predict the runtime characteristics of each of the quantum jobs, and using the prioritization engine to prioritize the placeholders of the quantum jobs based on the runtime characteristics, and wherein the grace period is dynamically determined as a time required by the computer job to generate an output that serves as input for a corresponding quantum job for execution in a quantum processing unit;
- in response to a determination that a highest priority quantum job associated with a different application from the job queue is to be executed, obtaining substance of the highest priority quantum job by notifying the classical system to submit the highest priority quantum job;
- after obtaining the substance of the highest priority quantum job, sending the highest priority quantum job to the quantum processing unit for execution;
- and
- executing the highest priority quantum job during the determined grace period of the computer job of the application.
5 more elements
Dependencies are inferred from the claim text.