Hybrid Quantum-Optimization Engine for Global Solution Search
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Solution Overview
Problem
Classical optimization engines face difficulties in efficiently solving complex optimization tasks due to the presence of numerous local minima and the need to handle uncertainty, which limits their ability to find global solutions, especially for problems with a large number of variables.
Innovation Solution
A hybrid classical-quantum optimization engine that uses a classical processor to obtain local solutions and a quantum computing device to perform a quantum genetic algorithm, applying quantum selection, mutation, and crossover operations to generate global solutions by encoding and processing data in a quantum domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical optimization engines are used to solve complex optimization tasks, then the computation can be performed using classical algorithms, but the ability to find global solutions is limited due to numerous local minima
Solution Approach 1:
The patent introduces a quantum computing device as an intermediary component between the classical processor and the optimization problem. The quantum device receives local solutions from the classical engine, performs quantum genetic operations (selection, mutation, crossover) to explore the solution space more effectively, and returns global solutions to the classical engine. This intermediary quantum system resolves the contradiction by providing enhanced global search capability while maintaining compatibility with classical optimization frameworks.
2Productivity
If classical algorithms are used to handle uncertainty in optimization tasks, then the implementation is straightforward, but the efficiency and accuracy are limited
Solution Approach 1:
The patent segments the optimization system into two distinct functional components: a classical optimization engine that handles straightforward implementation and local solution generation, and a quantum computing device that handles complex quantum genetic operations for improving efficiency. This segmentation allows each component to specialize in its strengths, resolving the contradiction between implementation simplicity and computational efficiency.
Solution Approach 2:
The patent merges classical and quantum computing approaches into a hybrid system. The classical processor and quantum computing device work together in an iterative framework where local solutions are combined with quantum genetic operations to produce improved global solutions. This merging resolves the contradiction by combining the simplicity of classical implementation with the efficiency gains of quantum computation.
3Measurement precision
If more quantum operations are performed to explore infinite states, then the accuracy of global solutions improves, but the computational time and resources increase
Solution Approach 1:
The patent applies partial quantum genetic operations rather than exhaustive exploration of all possible quantum states. The quantum computing device performs a limited number of selection, mutation, and crossover operations on the local solutions provided by the classical engine, sufficient to improve accuracy without requiring complete exploration of the entire solution space. This partial action resolves the contradiction between accuracy improvement and computational time consumption.
Data Source
AI summary
Apparatus and methods for solving global optimization tasks using quantum computing. In one aspect, a method includes receiving input data, the input data comprising (i) data representing one or more local solutions to the global optimization task, and (ii) data representing one or more task objectives; mapping the received input data to a quantum domain; performing a genetic algorithm on the mapped input data using the quantum computing device to obtain a solution to the global optimization task in the quantum domain; and obtaining a solution to the global optimization task in a classical domain by mapping the obtained solution to the global optimization task in the quantum domain to the classical domain.