Hybrid Quantum-Classical Routing Stack for Vehicle Logistics
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Solution Overview
Problem
Classical computing architectures face inefficiencies and scalability issues in solving complex vehicle routing problems with time windows, particularly when the number of nodes exceeds a certain threshold, leading to exponential solution times and significant gaps between identified and ideal routing solutions.
Innovation Solution
A hybrid quantum-classical computing platform is employed, where a classical computing device generates feasible routes and distributes them into bags based on the capacity of a quantum computer, allowing the quantum computer to calculate the most efficient route combination using quantum processing units (qubits) to cover all nodes efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional computing architectures are used to solve vehicle routing problems, then the problem can be solved with existing technology, but the solution time becomes exponential and efficiency deteriorates when the number of nodes exceeds a certain threshold
Solution Approach 1:
The patent segments the vehicle routing problem into two distinct parts: (1) generating feasible routes using classical computing, and (2) selecting the optimal combination of routes using quantum computing. This segmentation allows each computing system to operate in its optimal domain, avoiding the exponential time complexity that would result from using classical computing alone for the entire optimization problem.
Solution Approach 2:
The patent introduces a quantum computer as an intermediary component in the hybrid computing architecture. The quantum computer receives feasible routes from the classical computer and performs quantum optimization to identify the optimal route combination. This intermediary quantum processing step enables solving the NP-hard optimization problem efficiently without requiring the classical computer to handle the full computational burden.
2Adaptability or versatility
If the number of nodes to be serviced increases, then the routing problem becomes more comprehensive and useful, but the complexity of the problem increases leading to exponential solution times
Solution Approach 1:
The patent divides the routing problem into route generation (classical computing) and route combination optimization (quantum computing). This segmentation allows the system to handle larger numbers of nodes by distributing computational tasks appropriately, preventing the exponential complexity from overwhelming a single classical computing system.
Solution Approach 2:
The patent changes the computational paradigm from classical to quantum for the optimization phase. By utilizing quantum mechanical properties such as superposition and entanglement, the quantum computer can evaluate multiple route combinations simultaneously, effectively reducing the computational complexity parameter for large-scale routing problems.
3Ease of manufacture
If classical computing methods are used for route optimization, then the implementation is straightforward with existing technology, but the gap between identified and ideal routing solutions increases significantly
Solution Approach 1:
The quantum computer serves as an intermediary that bridges the gap between feasible routes generated by classical methods and the ideal optimal solution. The quantum optimization process explores the solution space more thoroughly and identifies superior route combinations that classical methods cannot find, thereby improving solution quality while maintaining implementation feasibility through the hybrid architecture.
Solution Approach 2:
The patent creates a composite computing system that combines classical and quantum computing components. This hybrid architecture leverages the strengths of both systems: classical computing for deterministic route generation and quantum computing for probabilistic optimization, resulting in a solution quality that exceeds what either system could achieve alone.
4Productivity
If a quantum computer is used to solve the entire routing problem, then the most efficient solution can be found, but the quantum computer capacity is insufficient for handling large numbers of nodes directly
Solution Approach 1:
The patent segments the routing problem so that only the critical optimization component is assigned to the quantum computer, while the route generation component remains with the classical computer. This segmentation reduces the number of qubits required for quantum processing, making the solution feasible with current quantum hardware limitations while still achieving significant optimization efficiency gains.
Solution Approach 2:
Instead of using the quantum computer to generate all possible routes (excessive action), the patent uses it only to optimize the selection from a pre-generated set of feasible routes (partial action). This approach achieves the necessary optimization efficiency without requiring quantum computer capacity proportional to the total problem size, thereby working within current hardware constraints.
Data Source
AI summary
A method and system of generating a route includes receiving information regarding a set of nodes to be serviced. One or more parameters of each node are determined. A capacity of each of the one or more vehicles is determined. A classical computer is used to generate a set of feasible routes based on the one or more parameters of each node and the capacity of each of the one or more vehicles. A number of bags N to divide the set of feasible routes is determined. The feasible routes are distributed into the N bags. The N bags are sent to a quantum computer to calculate a most efficient combination of feasible routes that cover all nodes to be serviced.