Quantum Job Orchestration Across Multi-Cloud QaaS
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Solution Overview
Problem
Users face challenges in selecting an appropriate Quantum as a Service (QaaS) vendor due to varying quantum processing unit capabilities, availabilities, and costs, making it difficult to maximize the use of quantum computing systems and meet user-specific criteria such as budget and execution deadlines.
Innovation Solution
An orchestration engine that consolidates queue status across multiple QaaS providers, predicts runtime characteristics using machine learning models, and optimizes quantum job placement across different QPUs and vendors based on user intents and vendor criteria, allowing for real-time decision-making and continuous learning to improve resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select QaaS vendors based on varying capabilities and costs, then user-specific criteria such as budget and execution deadline can be considered, but the complexity of informed selection increases and resource utilization efficiency decreases
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between users and QaaS vendors. This system consolidates queue statuses from multiple vendors, predicts runtime characteristics using machine learning, and automatically matches quantum jobs with optimal QPU resources based on user intents and vendor criteria, thereby simplifying the vendor selection process while maintaining informed decision-making
Solution Approach 2:
The system enables self-service by allowing quantum jobs to be automatically placed and executed on appropriate QPUs without manual user intervention. The orchestration engine autonomously evaluates vendor criteria, predicts runtime characteristics, and assigns jobs based on user intents, freeing users from the complexity of manual vendor selection
2Productivity
If quantum jobs are distributed across multiple QaaS providers, then resource utilization of quantum computing systems can be maximized, but the difficulty of optimizing job placement across heterogeneous environments increases
Solution Approach 1:
The patent creates a universal orchestration system that can handle multiple QaaS providers and different QPU types through a unified interface. The system consolidates queue statuses from diverse vendors into a common telemetry plane and applies universal machine learning models to predict runtime characteristics, enabling optimized job placement across heterogeneous quantum environments without provider-specific complexity
Solution Approach 2:
The system dynamically changes parameters such as queue status, runtime characteristics, and vendor criteria based on real-time conditions. By continuously monitoring and adjusting these parameters, the orchestration engine can optimize job placement decisions across multiple providers, adapting to changing system states and maximizing resource utilization
3Loss of time
If real-time optimization of quantum job placement is implemented, then wait times and cost efficiency can be improved, but the computational overhead for predicting runtime characteristics and making placement decisions increases
Solution Approach 1:
The system performs preliminary actions by pre-consolidating queue statuses from multiple vendors into a telemetry plane before job submission. Machine learning models are trained in advance on historical quantum job data to predict runtime characteristics, so that when jobs need placement, the system can quickly retrieve pre-processed information and make rapid decisions without heavy real-time computational overhead
Data Source
AI summary
Global optimization of quantum jobs in a multi-cloud or multi-edge environment is disclosed. The quantum jobs of multiple vendors are consolidated in a telemetry plane. The quantum jobs are evaluated based on user intents, quantum job characteristics, and quantum processing unit characteristics. The quantum jobs are then assigned to the quantum systems of the vendors based on the evaluation.