Hybrid Classical-Quantum Routing for Multi-Hop Cargo Logistics
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Solution Overview
Problem
Logistics route optimization, particularly multi-hop routing, is computationally challenging due to high constraint density exceeding the capability of quantum annealers, leading to inefficiencies and sub-optimal solutions.
Innovation Solution
A hybrid approach combining classical computing to reduce the search space by applying routing constraints, followed by quantum annealing to explore a refined search space, allowing for more effective problem-solving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum annealing is applied directly to multi-hop routing problems, then route optimization capability is enhanced, but constraint density exceeds the capability of quantum annealers leading to sub-optimal solutions
Solution Approach 1:
The patent segments the routing problem into two distinct phases: a classical preprocessing phase that handles constraint reduction and search space filtering, and a quantum annealing phase that optimizes the reduced problem. This segmentation allows each system to operate within its capability limits while achieving overall optimization precision.
Solution Approach 2:
The classical computing system performs preliminary actions by pre-filtering the search space and reducing constraint density before the quantum annealer processes the problem. This preliminary processing removes infeasible routes and simplifies constraints, enabling the quantum system to focus computational resources on finding optimal solutions rather than navigating excessive complexity.
2Productivity
If classical computing is used alone for route optimization, then computational simplicity is maintained, but the ability to solve complex multi-hop routing problems efficiently is insufficient
Solution Approach 1:
The patent merges classical and quantum computing systems into a hybrid architecture where each system contributes its strengths. The classical system handles preprocessing and constraint management, while the quantum system performs optimization on the reduced search space, achieving high productivity without overwhelming computational complexity.
Solution Approach 2:
The classical computing system acts as an intermediary that transforms the complex routing problem into a simplified form suitable for quantum processing. It mediates between the original problem specification and the quantum annealer's capabilities by filtering and restructuring the input data.
3Adaptability or versatility
If the search space is not reduced, then all possible routes can be explored, but the constraint density threshold of the quantum annealer prevents effective exploration
Solution Approach 1:
The patent applies local quality by selectively filtering the search space based on local constraints and feasibility criteria. Rather than uniformly processing all possible routes, the classical system identifies and removes locally infeasible options, allowing the quantum annealer to explore the remaining viable routes with reduced constraint density.
Data Source
AI summary
An optimal route can be determined for delivering an item through a logistics system where vehicles have multiple stops when traveling along routes. The optimal route is determined using a hybrid system employing a classical computing device and a quantum annealer. The classical device reduces the search space that allows the quantum annealer to determine a more optimal solution. In an aspect, a routing graph comprising nodes and edges is populated from routes of vehicles, where the nodes identify locations and the edges represent routing data. At least a portion of the nodes and edges is removed to form a refined routing graph. For an origin-destination input, the refined routing graph can be filtered according to a first set of routing constraints to form a reduced route search space. A quantum annealer is invoked according to an objective and a different second set of routing constraints.