Quantum Job Orchestrator for Hybrid Cloud Resource Allocation
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Solution Overview
Problem
Current cloud computing environments are not operable without a quantum-specific mechanism to direct jobs to quantum compute nodes (QCNs) or combine results from QCNs and conventional nodes (CNs) in hybrid clouds, leading to inefficiencies in data processing.
Innovation Solution
A method and system that ascertain compatibility between quantum processor configurations and job instructions, construct quantum instructions, execute them on QCNs, transform signals, and combine results with conventional results to produce a final output, effectively managing quantum and conventional computing resources in a hybrid cloud environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum computing resources are integrated into cloud environments, then computational capability for specific problems is improved, but system complexity and operational difficulty increase
Solution Approach 1:
The system segments jobs into quantum-processing portions and conventional-processing portions, directing each to appropriate compute nodes. This segmentation allows quantum resources to be used only where beneficial, maintaining productivity improvements while managing system complexity through modular job distribution.
Solution Approach 2:
A quantum job orchestrator acts as an intermediary between job submitters and hybrid quantum-conventional compute nodes. This orchestrator manages the complexity of coordinating quantum and conventional resources, translating high-level job descriptions into specific quantum and conventional instruction sequences, and combining results appropriately.
2Reliability
If quantum-specific mechanisms are implemented to direct jobs to quantum compute nodes, then job processing accuracy is improved, but device complexity increases
Solution Approach 1:
The quantum job orchestrator provides multi-functional capabilities: it analyzes job descriptions, determines quantum suitability, compiles quantum instructions, manages quantum-conventional job distribution, and combines results. This universal orchestrator handles multiple responsibilities that would otherwise require separate mechanisms, improving reliability while controlling complexity.
Solution Approach 2:
The system performs preliminary analysis of job descriptions to identify portions suitable for quantum processing before actual execution. This preliminary action includes determining quantum-conventional job distribution strategies and preparing appropriate instruction sequences, ensuring accurate job processing from the start while avoiding complex runtime adjustments.
3Productivity
If jobs are split between quantum and conventional compute nodes, then resource utilization efficiency is improved, but operational complexity increases
Solution Approach 1:
The system dynamically determines job distribution between quantum and conventional compute nodes based on job characteristics, quantum resource availability, and problem types. This dynamic allocation optimizes resource utilization efficiency while the automated orchestrator manages operational complexity by adapting to changing conditions without manual intervention.
Solution Approach 2:
The quantum job orchestrator automatically performs job analysis, quantum suitability determination, and instruction compilation without requiring manual operational intervention. This self-service capability improves resource utilization efficiency while reducing operational complexity by eliminating the need for manual job distribution decisions.
4Measurement precision
If quantum instructions are constructed and executed separately from conventional instructions, then computing accuracy is improved, but processing time increases
Solution Approach 1:
The system merges quantum instruction execution and conventional instruction execution into a coordinated hybrid workflow managed by the orchestrator. Quantum portions and conventional portions are processed in an integrated manner with results combined systematically, maintaining computing accuracy while reducing overall processing time through parallel and pipelined execution strategies.
Data Source
AI summary
A compatibility is ascertained between a configuration of a quantum processor (q-processor) of a quantum cloud compute node (QCCN) in a quantum cloud environment (QCE) and an operation requested in a first instruction in a portion (q-portion) of a job submitted to the QCE, the QCE including the QCCN and a conventional compute node (CCN), the CCN including a conventional processor configured for binary computations. In response to the ascertaining, a quantum instruction (q-instruction) is constructed corresponding to the first instruction. The q-instruction is executed using the q-processor of the QCCN to produce a quantum output signal (q-signal). The q-signal is transformed into a corresponding quantum computing result (q-result). A final result is returned to a submitting system that submitted the job, wherein the final result comprises the q-result.