In order to encourage more people to use an annealing machine, we have published an article explaining the process of solving optimization problems with annealing machines for beginners.
We will explain the procedures and points for each of the four processes of " organizing problems and defining requirements," "formulation," "input data preparation," and "executing CMOS Annealing Machine".
The work of defining the problem in order to properly communicate the actual task to the computer is explained with the theme of a "school class timetable"
The formulation of the Ising model necessary for the annealing machine to solve the problem will be discussed on the topic of the "number partitioning problem".
The process of preparing the data to be input to run the annealing machine is explained under the theme of "number partitioning problem".
The process of inputting data from the created number division problem into a CMOS annealing machine, executing it, and reading the results is explained.
Take your learning one step further by experiencing the development flow of a combinatorial optimization system using a CMOS annealing machine through a series of steps from requirement definition to execution.
This article provides an exercise to realize a signal control system to relieve traffic congestion as a signal control system by processing it with a CMOS annealing machine as a combinatorial optimization problem.
Combinatorial optimization will be explained by using two easy-to-understand examples of familiar combinatorial optimization problems. The importance of finding and using fast solution methods for combinatorial optimization processes in an IoT society will also be discussed.
In the wake of the COVID-19 epidemic, we present a use case in which an annealing machine is used to create shifts for researchers under constraints that take into account the risk of infection.
This article presents a use case in which an annealing machine is used to formulate a reinsurance portfolio that leverages the vast amount of data held by an insurance company to address natural catastrophe risks.
This article provides an easy-to-understand explanation of the mathematical assumptions you need to understand, focusing on "quadratic" and "discrete," which are some of the characteristics of the problems that annealing machines excel at solving.