Hybrid Quantum-Classical Max-Cut Solving with Precomputed Subgraphs
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Solution Overview
Problem
Current quantum computing technology faces limitations in solving large-scale combinatorial optimization problems like Max-Cut due to limited quantum resources and the need for multiple costly runs, especially when using QAOA, which is compounded by the computational burden of divide-and-conquer approaches.
Innovation Solution
A hybrid quantum-classical system that segments large graphs into subgraphs, checks for precomputed solutions in a database, leverages structural similarities, and uses quantum computing only when necessary, with parallel processing by GPUs, optimizing efficiency and reducing quantum resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum computing is used to solve large-scale combinatorial optimization problems, then computational capability is improved, but quantum resource consumption and computational cost increase
Solution Approach 1:
The patent divides large-scale combinatorial optimization problems into smaller sub-problems by segmenting the input data or problem structure. This allows the quantum computer to process smaller instances that are more manageable, reducing the quantum resource consumption while still making progress on the overall complex problem through iterative or hierarchical processing.
Solution Approach 2:
The patent performs preliminary computations or pre-processing on classical computers to prepare data, reduce problem size, or identify simplified versions of the optimization problem before feeding it to the quantum computer. This preliminary action reduces the burden on quantum resources by handling the most computationally intensive parts classically.
2Device complexity
If divide-and-conquer approach is used to solve combinatorial optimization problems, then problem complexity is reduced, but computational burden increases
Solution Approach 1:
The patent merges the strengths of classical and quantum computing by combining classical divide-and-conquer strategies with quantum computational power. Classical computers handle the division of problems into manageable parts, while quantum computers provide accelerated solving for the sub-problems, achieving both reduced complexity and lower computational burden through hybrid processing.
Solution Approach 2:
The patent introduces an intermediary layer between problem decomposition and quantum processing. This intermediary layer (implemented through classical computing algorithms) optimizes how problems are divided and prepared for quantum input, acting as a mediator that translates complex problems into forms suitable for quantum processing while managing computational resources efficiently.
3Measurement precision
If multiple quantum runs are performed to obtain accurate solutions, then solution accuracy is improved, but computational cost and time increase
Solution Approach 1:
The patent applies partial action by performing a limited number of quantum runs or using approximate quantum algorithms that provide sufficiently accurate solutions without requiring exhaustive searches. This partial approach balances the need for acceptable solution accuracy with the constraint of reducing computational time and avoiding excessive quantum resource consumption.
Solution Approach 2:
The patent incorporates feedback mechanisms where the results of quantum runs are analyzed and used to guide subsequent computations. By using feedback from earlier runs to optimize later runs (through parameter adjustment or search space reduction), the system achieves improved solution accuracy while reducing the total number of required quantum runs and associated computational time.
Data Source
AI summary
Systems, computer program products, and methods are described for a hybrid quantum-classical system for enhanced combinatorial optimization. An example system segments a received task into multiple sub-tasks. For each sub-task, the system accesses a database of pre-computed solutions through the classical computing unit to identify a suitable pre-computed solution. In scenarios where a pre-computed solution is not available for a sub-task, the classical computing unit transmits this sub-task to a quantum computing unit. The computing unit, utilizing a quantum optimization algorithm, computes a solution for the sub-task. This solution is then relayed back to the classical computing unit. The classical computing unit then implements each identified pre-computed and newly computed solution on the combinatorial optimization task.