Hybrid Quantum Computing for Accurate Combinatorial Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Classical exhaustive search methods provide exact solutions for combinatorial optimization problems but at exponential time complexity, while quantum algorithms like VQE or QAOA offer polynomial time complexity but may not ensure solution accuracy.
Innovation Solution
A hybrid quantum-classical computing apparatus uses a classical computer to store and transform optimization problems into an Ising model, and a quantum computer to obtain solutions using QAOA or VQE, followed by a greedy algorithm to improve the solution accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum algorithms (VQE or QAOA) are used to solve combinatorial optimization problems, then execution time is reduced to polynomial complexity, but solution accuracy cannot be ensured
Solution Approach 1:
The patent merges quantum computing and classical computing into a hybrid system. The quantum computer executes quantum algorithms (VQE or QAOA) to obtain initial solutions with polynomial time complexity, while the classical computer performs greedy algorithms to improve solution accuracy. This combination allows the system to achieve both fast execution and high accuracy by leveraging the strengths of both computing paradigms.
Solution Approach 2:
The classical computer acts as an intermediary between the quantum algorithm output and the final optimized solution. It receives solutions from the quantum computer and applies classical greedy optimization methods to refine them, serving as a bridge that transforms quantum computational speed into accurate final results.
2Measurement precision
If classical exhaustive search method is used to solve combinatorial optimization problems, then exact solution with 100% accuracy is obtained, but time complexity increases exponentially to O(n²2ⁿ)
Solution Approach 1:
The patent segments the solution process into two distinct phases: (1) quantum algorithm execution to generate initial candidate solutions with polynomial time complexity, and (2) classical greedy optimization to refine these solutions to exact accuracy. This segmentation allows each method to operate in its optimal performance zone, avoiding the exponential complexity of pure classical exhaustive search.
Solution Approach 2:
The quantum algorithm performs preliminary action by generating high-quality candidate solutions quickly. These pre-computed solutions serve as excellent starting points for the subsequent classical greedy optimization, significantly reducing the search space and enabling exact solutions to be reached much faster than pure exhaustive search.
Data Source
AI summary
The present disclosure relates to obtaining an enforced solution of a combinatorial optimization problem by a hybrid quantum-classical computing apparatus. The method of obtaining an enforced solution of a combinatorial optimization problem is performed by a hybrid quantum-classical computing apparatus that includes a classical computer and a quantum computer, and the method includes: by the classical computer, storing a representation of a combinatorial optimization problem that is to be solved; by the quantum computer, obtaining a solution of the combinatorial optimization problem using a quantum algorithm; and by the classical computer, improving the solution, obtained using the quantum algorithm, by executing a greedy algorithm to obtain an enforced solution based on the solution of the combinatorial optimization problem.