Hybrid Computing Resource Optimization via Directed Graph Pathfinding
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Solution Overview
Problem
Current hybrid computational approaches are devised by hand, relying on trial-and-error, and lack a quantitative method to determine optimal arrangements of sub-processes across classical and quantum computing resources.
Innovation Solution
A computer-implemented method and system that generates a directed graph network representing computational sub-tasks with varying resources, and solves an optimization problem to determine a best path for achieving computational objectives, such as minimizing time or error, while estimating required resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hand-devised trial-and-error approaches are used to create hybrid computational approaches, then flexibility in designing computational workflows is maintained, but the ability to determine optimal arrangements of sub-processes quantitatively is lost
Solution Approach 1:
The computational task is segmented into multiple sub-processes represented as nodes in a directed graph, each node representing a specific computational operation. This segmentation allows the system to analyze and optimize each sub-process independently while maintaining the overall workflow structure, enabling quantitative determination of optimal arrangements without creating an entirely complex new system.
Solution Approach 2:
The system dynamically generates directed graphs representing computational workflows and automatically solves optimization problems to determine the best path through the graph. This dynamic approach allows the system to adapt to different computational objectives and resource constraints, providing quantitative optimization while maintaining manageable complexity through automated algorithms.
2Speed
If quantum computing resources are used to solve hard problems, then computational speed is increased, but the noise in quantum hardware makes robust computation difficult
Solution Approach 1:
The system merges classical and quantum computing resources into a hybrid computational approach. Classical computing handles preprocessing, postprocessing, and error correction, while quantum computing performs specific computational sub-tasks that benefit from quantum speedup. This combination maintains the speed advantage of quantum computing while using classical resources to ensure robustness and reduce noise impacts.
Solution Approach 2:
Classical computing acts as an intermediary between the quantum computational sub-tasks, handling the coordination, error correction, and result interpretation. This intermediary layer protects the quantum computations from noise and hardware imperfections while still allowing the quantum resources to provide their speed advantage for specific computational problems.
3Productivity
If hybrid arrangements of classical and quantum computing resources are used, then the best capabilities of each resource are leveraged, but the complexity of determining optimal resource allocation increases
Solution Approach 1:
The system automatically determines optimal resource allocation by solving optimization problems on the generated directed graphs. The computational workflow self-optimizes by automatically identifying the best path through the graph that balances classical and quantum resource usage, eliminating the need for manual configuration and reducing the complexity of resource allocation determination.
Solution Approach 2:
The system changes parameters such as computational time, error rates, and resource requirements to formulate and solve optimization problems. By dynamically adjusting these parameters based on the specific computational task and available resources, the system achieves optimal productivity while managing complexity through automated parameter optimization rather than manual configuration.
Data Source
AI summary
Arranging computational sub-tasks in a hybrid-computing environment is provided. The method comprises receiving input of a number of nodes, wherein each node represents a computational sub-task, and wherein the nodes are grouped into different sets according to differing computing resources used by the nodes. A computational objective is also received as well as initial data inputs and desired final outputs. A directed graph network is generated comprising the nodes and directed edges connecting the nodes. An optimization problem is then solved to determine a best path through the directed graph network for deriving the desired final outputs from the initial data inputs according to the computational objective. The best path comprises a subset of nodes and directed edges within the directed graph network. The best path to achieve the computational objective and estimated computational resources required for the best path are output to a user.