Quantum Concept Processor Routing Optimization
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Solution Overview
Problem
Current techniques for optimizing data traffic routing in communications networks face challenges such as high computational complexity, inefficiencies in capacity utilization, and overloads in link capacities, especially when dealing with non-linear conditions and complex network configurations.
Innovation Solution
A computer-implemented method that utilizes a quantum concept processor to optimize data traffic routing by capturing traffic demands, specifying potential short communication paths, calculating fractional capacity usages, formulating these usages into a quadratic stress function, and selecting optimized paths to minimize the stress function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If linear optimization techniques are applied to minimize maximal link capacity usage, then capacity utilization is improved, but the computational complexity becomes NP-hard and the system reaches limits quickly when non-linear conditions are considered
Solution Approach 1:
The patent transforms the routing optimization problem from a linear optimization framework to a quadratic optimization framework by introducing a quadratic stress function. This parameter change in the mathematical model enables the system to handle non-linear conditions (such as redundant paths, geographical subgroups, and QoS requirements) while maintaining computational tractability through quantum annealing, thus resolving the contradiction between improved capacity utilization and computational complexity.
2Adaptability or versatility
If reactive link weight manipulation is used to guide traffic demands, then routing adaptability is improved, but the system responds slowly and cannot achieve global optimization
Solution Approach 1:
The patent performs preliminary action by pre-calculating a set of potential short communication paths between origin-destination pairs before actual traffic routing occurs. These pre-computed paths are stored and ready for selection, enabling the system to quickly respond to traffic demands and network changes without performing complex real-time optimization, thus achieving both adaptability and speed.
Solution Approach 2:
Instead of computing all possible paths between origin and destination nodes (excessive action), the patent computes only a limited set of potential short paths (partial action). This selective approach reduces computational burden while providing sufficient routing options to achieve near-optimal solutions, balancing adaptability with computational efficiency.
3Ease of operation
If shortest path routing based on link weights is applied, then routing simplicity is improved, but link overloads occur and capacity limits are exceeded
Solution Approach 1:
The patent introduces a quadratic stress function as an intermediary between the simple shortest path routing and the capacity constraint requirements. This stress function acts as a mediator that guides the selection of paths from the pre-computed set, penalizing routes that would lead to link overloads while maintaining the simplicity of working with a predefined path set. The quantum annealing process optimizes path selection based on this intermediary function, achieving both simplicity and reliability.
Data Source
AI summary
A computer-implemented method optimizes a routing of data traffic in a communications network by using a quantum concept processor. A set of potential short communication paths among possible communication paths between respective origin nodes and respective destination nodes of captured traffic demands is specified. The edges within the set of potential short communication paths are assigned a respective usage capacity limit. Fractional capacity usages of the edges are calculated based on respective usage capacity limits of the edges. The calculated fractional capacity usages are formulated as terms of a quadratic stress function. An optimized routing is determined by using a quantum concept processor, thereby selecting for each traffic demand one short communication path from the set of potential short communication paths such that the quadratic stress function is minimized.