For Fujitsu, computational science research is one of the cornerstones of a successful IT service provider. Whether it is the development of quantum computers, new solutions for more sustainable and effective mobility, energy-saving HPC systems or the Digital Factory – alone and with international partners, Fujitsu always aims to create positive added value for society, the environment, and its customers. This is why Fujitsu regularly participates in European, more research-oriented tenders and projects with a wide range of partners.
As part of the FaaS paradigm that applies to the “PHYSICS” platform, Fujitsu has set itself the goal of contributing various optimization functions. The term optimization can be understood to cover a wide range of activities or functions: an optimized process, the provision of specific data to be able to make (optimized) decisions more quickly, the optimal use of time or the optimized packaging of content in a package. From a mathematical point of view, the latter is a real optimization problem, more precisely a problem from the field of combinatorial optimization. Combinatorial optimization problems include sequencing, assignment, grouping and selection problems. These include, for example, the traveling salesman problem, the Knapsack problem, graph similarity, portfolio optimization, (scheduling) planning, (task) assignment, software validation, AI model optimization and many others. Behind these somewhat stubborn names are everyday problems from logistics, the packaging industry, the manufacturing industry, shift planning and many others. What all these problems have in common is that there is not just one solution to them, but that there may well be many qualitatively different solutions. Furthermore, there is no known algorithm that can simply calculate these problems directly. Finding a good or the best solution in large problem spaces therefore requires an enormous amount of computing capacity and time.
As part of the “PHYSICS” project, Fujitsu has developed various optimization patterns that benefit from the performance of the quantum-inspired technology “Digital Annealer“. The Digital Annealer can solve combinatorial optimization problems particularly quickly and with particularly good results. To do this, it uses several ideas from annealing and quantum computing to solve the problems described above particularly well and quickly. But first, the problem in question is converted into a specific mathematical model, which the digital annealer can then solve. The highlight: if quantum computers are one day able to solve similarly large problems, the mathematical model already developed can simply be transferred to them and solved. The technology therefore not only provides a relevant advantage in business today, but also prepares the company in the best possible way for the age of quantum computers.
The physics project now provides two patterns for optimization problems and their solution with the help of the Digital Annealer in Node-Red. These processes can be transferred to the runtime environment with just a single click. To simplify the use of the Digital Annealer and its integration into the Node-Red ecosystem, we have implemented various backbone classes that enable the use of the Digital Annealer and the processing of optimization problems within Node-Red.