Ising Machine Optimization via Variable Derivation
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Solution Overview
Problem
Ising machines are limited in the scale of optimization problems they can solve due to the difficulty in increasing the number of quantum bits, which restricts their ability to handle larger problems compared to classical computers.
Innovation Solution
An information processing apparatus and method that uses a second objective function to derive a minimum value for unknown variables, allowing the Ising machine to solve optimization problems with a reduced number of quantum bits by modifying the mathematical model and relaxing constraint conditions, thereby reducing the amount of calculation required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of quantum bits is increased to solve larger optimization problems, then the problem-solving capability of the Ising machine is improved, but the device complexity and difficulty of increasing quantum bits worsens
Solution Approach 1:
The patent divides the optimization problem into multiple sub-problems that can be solved independently with fewer quantum bits. By segmenting the problem, the Ising machine can handle larger overall problems without requiring a proportional increase in quantum bit count, thus resolving the contradiction between problem-solving capability and device complexity
Solution Approach 2:
The patent introduces a new dimension of problem representation by using a different mathematical model formulation. Instead of directly mapping the entire problem to quantum bits, it transforms the problem into a form where constraints are relaxed and variables are redefined, enabling solution of larger problems within limited quantum bit resources
2Adaptability or versatility
If the number of quantum bits is increased to handle larger optimization problems, then the scale of solvable problems is improved, but the difficulty of increasing quantum bits worsens
Solution Approach 1:
The patent changes key parameters of the problem formulation, including relaxing constraint conditions and redefining variable relationships. This parameter transformation allows the same quantum bit resources to solve larger-scale problems by reducing the computational burden per quantum bit through mathematical reformulation
3Productivity
If constraint conditions are relaxed to reduce calculation amount, then the calculation efficiency is improved, but the feasibility of solutions may worsen
Solution Approach 1:
The patent performs preliminary actions by pre-processing the problem to identify and relax only those constraints that are non-critical to solution feasibility. This selective relaxation maintains solution validity while reducing computational complexity, thus improving calculation efficiency without sacrificing solution feasibility
Solution Approach 2:
The patent implements a feedback mechanism where the Ising machine iteratively solves the relaxed problem and the results are validated against the original constraints. If solutions violate critical constraints, the model is adjusted and re-solved, ensuring final solution feasibility while maintaining the efficiency benefits of relaxed constraints during the solving process
Data Source
AI summary
In a case of causing an Ising machine to execute a process of solving an optimization problem by obtaining a value of an unknown variable using a first objective function, the value of the unknown variable being correlated with the number of quantum bits used in solving the optimization problem using the Ising machine, an information processing apparatus derives a smallest value for the unknown variable within a range in which a feasible solution of the optimization problem is obtained by using a second objective function different from the first objective function, and performs control of causing the Ising machine to execute a process of solving the optimization problem defined by a mathematical model including the derived value for the unknown variable and the first objective function.