Tensor Network Configuration Search for Unconstrained Optimization
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Solution Overview
Problem
Existing methods struggle to efficiently solve unconstrained optimization problems, particularly those with multiple variables and complex relationships, due to high computational complexity, making it difficult to find superior configurations for processes and systems in a timely manner.
Innovation Solution
A computer-implemented method using a Tensor Network to convert discrete variable cost function equations into unconstrained optimization problems, iteratively modifying tensor coefficients with different sets of parameters to reduce the cost function value, and checking for convergence criteria to achieve optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If quantum computing methods are used to solve optimization problems, then solution speed is improved, but device complexity increases
Solution Approach 1:
The patent introduces a quantum processor as an intermediary component that specifically handles the quantum annealing computation, while classical processors manage the overall optimization workflow, data preprocessing, and result interpretation. This mediator approach allows the system to leverage quantum speedup for the core optimization task without requiring the entire system to be quantum, thus improving solution speed for optimization problems while keeping overall device complexity manageable through specialized architecture.
2Manufacturing precision
If tensor network methods are used to solve unconstrained optimization problems, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the optimization problem into discrete variables that can be represented and manipulated within the tensor network framework. By breaking down complex optimization problems into manageable discrete components, the system achieves high precision in finding optimal configurations while keeping the computational device complexity tractable through structured tensor representations and efficient contraction algorithms.
3Productivity
If multiple variables with complex relationships are optimized, then productivity is improved, but loss of time increases
Solution Approach 1:
The patent replaces traditional classical computational mechanics with quantum annealing mechanics to solve optimization problems involving multiple variables with complex relationships. The quantum system naturally explores the solution space through quantum tunneling and thermal annealing processes, finding optimal configurations faster than classical algorithms, thus improving productivity in optimizing multi-variable systems while reducing the time loss associated with complex computations.
Data Source
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AI summary
A computer-implemented method whereby an equation with a cost function for minimization is solved by means of a tensor network. Coefficients of tensors of the tensor network are modified so as to reduce a value of the cost function in an iterative process until convergence is reached, at which point the concerned Unconstrained Optimization problem is solved and the values of the variables of the cost function are provided.