Inverse Temperature Estimation in Pseudo-Quantum Annealing
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Solution Overview
Problem
Existing methods for solving constraint-based combinatorial optimization problems using pseudo-quantum annealing are inefficient due to the inability to appropriately estimate the inverse temperature, leading to prolonged solution times.
Innovation Solution
An information processing apparatus and method that calculates flip energy changes, transition energy changes, and inverse temperatures based on constraint conditions in combinatorial optimization problems, using an objective function and constraint terms to optimize the pseudo-quantum annealing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pseudo-quantum annealing is used to solve constraint-based combinatorial optimization problems with standard inverse temperature estimation, then the optimization process can proceed, but the solution time becomes excessively long
Solution Approach 1:
The patent changes the parameter estimation approach by calculating inverse temperature based on transition energy change between current and next solutions, rather than using standard estimation methods. This involves computing the difference in energy values before and after solution transitions, then deriving the inverse temperature parameter from this transition energy change, which significantly improves solution efficiency
Solution Approach 2:
The patent performs preliminary calculation of transition energy change before the actual optimization search. By pre-computing the energy difference between current and next solutions, the system prepares the inverse temperature parameter in advance, avoiding iterative estimation during the optimization process and thereby reducing overall solution time
Data Source
AI summary
An information processing apparatus of the present disclosure includes: a first calculating unit that calculates a flip energy change, which is an energy change when a constraint condition is satisfied and each spin flips, using an objective function of a formulated model representing energy in a combinatorial optimization problem with the constraint condition; a second calculating unit that calculates a transition energy change, which is an energy change at a time of transitioning to a next solution in the combinatorial optimization problem, based on the flip energy change; and a third calculating unit that calculates an inverse temperature used at a time of solving the optimization problem by pseudo-quantum annealing, based on the transition energy change.