Iterative Quantum Annealing Engine for QUBO Model Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Quantum annealing lacks support for warm starting and iterative processing, restricting its application and combination with classical methods for solving combinatorial optimization problems.
Innovation Solution
Implementing iterative quantum annealing by constructing a new QUBO model based on an initial solution, iteratively processing it with a quantum annealer, and using stopping criteria to determine solution quality and iteration necessity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If quantum annealing is applied directly without iterative processing, then the quantum annealer can operate independently, but it lacks the ability to improve solutions iteratively and integrate with classical methods
Solution Approach 1:
The patent merges quantum annealing with classical optimization methods by creating an iterative framework where the quantum annealer and classical processor work together. The classical processor constructs QUBO models from initial solutions and integrates quantum results back into the optimization process, enabling hybrid classical-quantum optimization.
Solution Approach 2:
The system implements dynamic iterative processing where the optimization process can adapt and refine solutions across multiple iterations. The classical processor dynamically constructs new QUBO models based on previous solutions, and the quantum annealer dynamically processes these models to generate improved solutions, creating a flexible adaptive optimization system.
2Reliability
If quantum annealing processes each problem from scratch, then the quantum annealer operates simply, but it cannot leverage initial solutions or perform warm starting
Solution Approach 1:
The classical processor performs preliminary actions by constructing QUBO models from initial solutions before submitting them to the quantum annealer. This preliminary model construction incorporates existing knowledge or heuristic solutions, enabling warm starting and improving the quality of solutions obtained from quantum annealing.
Solution Approach 2:
The system implements feedback mechanisms where solutions from quantum annealing are fed back to the classical processor, which then uses this information to construct improved QUBO models for subsequent iterations. This feedback loop continuously refines solutions, leveraging both quantum and classical processing strengths.
3Productivity
If iterative quantum annealing is implemented, then solution improvement through multiple iterations is enabled, but additional processing steps and model construction are required
Solution Approach 1:
The optimization process is segmented into distinct functional components: the classical processor handles QUBO model construction and solution integration, while the quantum annealer handles quantum optimization processing. This segmentation allows each component to specialize in its strength, improving overall productivity through division of labor.
Solution Approach 2:
The classical processor acts as an intermediary that bridges the initial solution and the quantum annealer. It transforms classical solutions into QUBO models suitable for quantum processing, and transforms quantum results back into usable solutions, enabling effective communication and iteration between classical and quantum systems.
Data Source
AI summary
Embodiments implement iterative quantum annealing to provide a solution of an optimization. An annealing engine is located upstream of a quantum annealer (or a digital annealer, simulated annealer, or classical solver). The annealing engine is configured to process an initial solution to an original Quadratic Unconstrained Binary Optimization (QUBO) model, and thereby construct a second QUBO model. The second model is then fed to the quantum annealer, which returns a computed solution. The annealing engine constructs an intermediate solution from the computed solution and the second QUBO model. If the annealing engine determines a stopping criterion is satisfied by the intermediate solution, a final solution is constructed therefrom. If the annealing engine determines the stopping criterion is not satisfied, the second QUBO model is overwritten with the intermediate solution to form the basis for another iteration of QUBO model creation, quantum annealing, and evaluation of satisfaction of the stopping criterion.