Hybrid Computing Workflow Allocation Under Quantum Resource Constraints
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Solution Overview
Problem
Current hybrid computational approaches are devised through trial-and-error, lacking a quantitative method to determine the optimal arrangement of classical and quantum computing resources, leading to inefficiencies and suboptimal resource utilization.
Innovation Solution
A method and system for network optimization that generates workflow, scheduling, and resource assignment constraints to determine the best computational objective by solving an optimization problem, balancing classical and quantum computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If trial-and-error methods are used to design hybrid computational approaches, then flexibility in exploring different configurations is maintained, but resource utilization efficiency deteriorates due to lack of systematic optimization
Solution Approach 1:
The patent applies parameter changes by systematically varying computational parameters such as circuit depth, number of qubits, and algorithmic configurations to identify optimal settings for hybrid quantum-classical computational workflows, transitioning from trial-and-error to methodical parameter exploration
Solution Approach 2:
The patent implements dynamics by creating adaptive workflows that dynamically adjust the distribution of computational tasks between quantum and classical resources based on real-time performance metrics and resource availability, enabling flexible yet optimized resource allocation
2Speed
If quantum computing resources are utilized for specific computational tasks, then exponential speedup is achieved for certain problem classes, but resource availability and scalability worsen due to limited quantum hardware
Solution Approach 1:
The patent applies segmentation by dividing computational workflows into distinct quantum and classical segments, where only specific sub-tasks requiring quantum advantage are executed on quantum resources while other tasks run on classical systems, thereby optimizing the use of limited quantum hardware
Solution Approach 2:
The patent implements partial action by applying quantum computational resources only to the extent necessary for achieving speedup on specific problem portions, rather than attempting to fully utilize available quantum resources, thus balancing speedup benefits with resource conservation
3Productivity
If computational tasks are distributed across hybrid computing resources, then resource utilization improves, but system complexity increases due to coordination and scheduling requirements
Solution Approach 1:
The patent applies universality by designing a standardized workflow orchestration framework that can manage diverse computational tasks across different quantum and classical resources through unified interfaces and common scheduling mechanisms, reducing coordination complexity despite system heterogeneity
Solution Approach 2:
The patent implements an intermediary layer in the form of a workflow management system that mediates between task submission and resource execution, handling the complexity of coordination, scheduling, and resource allocation while presenting a simplified interface to users
Data Source
AI summary
Network optimization for arranging computational sub-tasks in a hybrid-computing environment is provided. The method comprises receiving input of a network of nodes and edges representing computational processes and their constituent information, wherein the nodes are grouped according to whether the nodes use classical computing resources or quantum computing resources. The method generates workflow constraints, scheduling constraints and computing resource assignment constraints. The method generates an objective function. An optimization problem is solved according to the objective function and all said constraints. The solution determines a best computational objective achieved, a selected computational workflow through the nodes, compute job scheduling, and assignment of the computational processes among the classical computing resources and quantum computing resources. The computational workflow is then executed to achieve the best computational objective according to the computed job scheduling and assignment of computational processes among the classical computing resources and quantum computing resources.