Hybrid Classical Quantum Computing System for Optimization
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Solution Overview
Problem
Current technologies face challenges in efficiently solving large-scale combinatorial optimization problems due to the exponential scaling of solution spaces, making it difficult for classical computers to find optimal solutions within feasible time frames, while quantum computers are limited by resource constraints.
Innovation Solution
A hybrid computer system combining classical and quantum computing resources, where classical resources reduce optimization problems to smaller sizes that can be efficiently processed by quantum computers using algorithms like QAOA, enabling the solution of large-scale optimization problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical computers are used to solve large-scale combinatorial optimization problems, then the solution space can be explored exhaustively, but the computational time becomes infeasible due to exponential scaling
Solution Approach 1:
The patent divides the optimization problem into two parts: a classical computer handles the formulation and reduction of the problem to a smaller size, while a quantum computer solves the reduced problem. This segmentation allows the classical computer to leverage its strength in problem analysis and the quantum computer to provide exponential speedup for the core optimization, resolving the contradiction between solution quality and computational time.
Solution Approach 2:
The patent introduces a hybrid classical-quantum computing system where the classical computer acts as an intermediary that reduces the optimization problem to a smaller instance suitable for quantum processing. This intermediary role enables the system to tackle large-scale problems by breaking them down into manageable quantum-friendly formats, effectively addressing the exponential scaling issue.
2Productivity
If quantum computers are used to solve optimization problems, then computational speed can be significantly improved, but the problem size is limited by available quantum resources
Solution Approach 1:
The patent segments the problem-solving task by using classical computers to handle the larger problem formulation and reduction, while quantum computers process only the reduced, smaller instance. This allows the quantum computer to operate within its resource constraints while still solving large-scale problems through the classical pre-processing that reduces the problem size.
Solution Approach 2:
The patent transitions from a purely quantum approach to a hybrid classical-quantum architecture, adding a classical computing dimension to the system. This dimensional expansion allows the system to leverage classical problem reduction techniques before quantum processing, effectively increasing the solvable problem size beyond what quantum resources alone could handle.
3Productivity
If a hybrid classical-quantum system is used, then large-scale optimization problems can be solved efficiently, but the system complexity increases
Solution Approach 1:
The patent positions the classical computer as an intermediary that handles problem formulation and reduction, while the quantum computer processes the reduced problem. This intermediary architecture simplifies the overall system by clearly dividing responsibilities: classical handling of large-scale problem analysis and quantum handling of the core optimization, making the hybrid system more manageable despite the increased complexity.
Solution Approach 2:
The patent extracts the problem reduction function from the quantum computer and places it in the classical computer. By taking out the problem formulation and reduction tasks from the quantum processing stage, the system can leverage classical computational strength to prepare problems for quantum solving, reducing the burden on quantum resources and simplifying the overall architecture.
Data Source
AI summary
In a general aspect, an optimization problem is solved using a hybrid computing system. A classical processor unit receives a first data structure that represents the optimization problem. The classical processor unit executes a branch-and-bound process on the first data structure to generate values for a first subset of elements of a solution to the optimization problem. A second data structure is generated based on the first data structure and the first subset of elements. The second data structure represents a reduced version of the optimization problem. A quantum processor unit and a classical processor unit are used to execute a quantum approximate optimization algorithm (QAOA) on the second data structure to generate values for a second subset of the elements of the solution to the optimization problem. The first subset and second subset are combined to obtain the solution to the optimization problem.