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 constraints, scheduling constraints, 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 computational efficiency and resource utilization deteriorate due to lack of optimization
Solution Approach 1:
The patent applies parameter changes by formulating an optimization problem that systematically varies computational parameters (workflow arrangements, resource allocations, scheduling decisions) to find optimal configurations. The objective function evaluates different parameter combinations while constraints ensure feasibility, replacing trial-and-error with directed parameter optimization that improves computational efficiency while maintaining adaptability.
Solution Approach 2:
The patent implements dynamics by creating an adaptive optimization framework that can dynamically adjust computational workflows and resource allocations based on objective function evaluations. The system dynamically explores the solution space and adapts configurations to achieve optimal performance, transforming static trial-and-error into a dynamic optimization process.
2Power
If more quantum computing resources are allocated to achieve exponential speedup, then computational power for specific problems improves, but hardware availability and reliability worsen due to current NISQ device limitations
Solution Approach 1:
The patent applies segmentation by dividing the computational workload into segments that can be executed on different computing resources. The optimization framework segments tasks to allocate only appropriate portions to quantum resources, avoiding over-allocation to unreliable NISQ devices while maintaining computational power where quantum advantage is achievable.
Solution Approach 2:
The patent implements universality by creating a hybrid computing framework that can execute computational tasks across multiple resource types (quantum and classical). The system universally handles different computing paradigms, allowing tasks to be flexibly allocated to the most appropriate resource based on availability and reliability, rather than being restricted to quantum resources alone.
3Productivity
If classical and quantum computing resources are combined in hybrid systems, then computational capabilities are enhanced, but system complexity increases making resource management difficult
Solution Approach 1:
The patent applies the intermediary principle by introducing an optimization framework as a mediator between computational tasks and hybrid resources. This intermediary system manages the complexity of resource allocation, workflow coordination, and scheduling automatically, shielding users from system complexity while enabling enhanced computational capabilities through coordinated use of classical and quantum resources.
Solution Approach 2:
The patent implements feedback by using an objective function that evaluates computational workflows and provides feedback to the optimization process. This feedback mechanism automatically adjusts resource allocation and workflow arrangements based on performance evaluation, reducing management complexity through self-adjusting control rather than manual intervention.
4Productivity
If computational workflows are optimized using quantitative methods, then resource utilization and efficiency improve, but the complexity of constraint generation and optimization problem formulation increases
Solution Approach 1:
The patent applies self-service by designing an optimization framework that automatically generates constraints and formulates optimization problems based on the computational task description. The system serves itself by autonomously creating the mathematical formulation without requiring manual intervention, thereby improving resource utilization while minimizing the burden of optimization formulation complexity on users.
Data Source
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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.