Hybrid Classical-Quantum System for Mixed Integer Optimization
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Solution Overview
Problem
Current methods for solving combinatorial optimization problems, particularly mixed integer optimization problems, are limited by their inability to handle inequality constraints and a mix of discrete and continuous variables, as they primarily rely on binary variables and classical algorithms that face exponential scalability issues.
Innovation Solution
A hybrid classical-quantum computing system is employed to generate decision variables, derive quantum state parameters, and measure intermediate quantum states to sample solutions, allowing for the evaluation and iteration of both discrete and continuous decision variables using a combination of classical and quantum processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If classical algorithms are used to solve mixed integer optimization problems, then the problems can be solved with current technology, but the computational complexity scales exponentially with the number of variables
Solution Approach 1:
The patent combines quantum computing resources with classical computing resources into a hybrid system. The quantum processor handles specific optimization subtasks while the classical processor manages other aspects, allowing the system to solve mixed integer optimization problems more efficiently than purely classical approaches while avoiding the full exponential scaling burden.
Solution Approach 2:
The patent substitutes quantum mechanical processes for classical computational processes in solving optimization problems. By using quantum superposition and entanglement to represent and manipulate solution states, the system can explore the solution space more efficiently than classical algorithms, reducing the exponential computational complexity.
2Productivity
If quantum processors are used to solve combinatorial optimization problems, then solution speed can be improved, but the ability to handle inequality constraints and mixed integer problems is limited
Solution Approach 1:
The patent segments the optimization problem into different components that can be handled by different processors. Discrete variables are processed by the quantum processor while continuous variables and inequality constraints are handled by the classical processor, allowing each component to be optimized for its specific requirements and enabling handling of mixed integer problems with constraints.
Solution Approach 2:
The hybrid quantum-classical system provides universal capability to handle multiple types of optimization problems including binary, mixed integer, and continuous optimization problems with various constraints. The system can adapt to different problem types by appropriately distributing tasks between quantum and classical processors.
3Ease of manufacture
If only binary variables are used in quantum optimization algorithms, then the algorithms can be implemented with current quantum processors, but the applicability to real-world problems is constrained
Solution Approach 1:
The patent implements a dynamic system where the type of variables being processed can change based on the problem requirements. The system can switch between handling purely discrete variables on the quantum processor and incorporating continuous variables processed classically, allowing adaptation to different problem types while maintaining implementation feasibility with current quantum hardware.
Data Source
AI summary
Solving mixed integer problems using a hybrid classical-quantum computing system includes generating a plurality of decision variables for a function associated with a combinatorial optimization problem by a first processor using an optimizer, and deriving at least one quantum state parameter for a quantum processor based upon one or more of the decision variables. The quantum processor is initiated in a quantum state based upon the at least one quantum state parameter. A plurality of intermediate quantum states of the quantum processor are measured using a plurality of quantum measurements of the quantum state to obtain a plurality of samples. The plurality of samples are evaluated by the first processor to obtain a measure of a quality of the quantum state and of one or more solutions to the combinatorial optimization problem.