Informatics and Mathematical Engineering

Quantum computers are expected to be used in solving large-scale optimization and design problems for which conventional computers are impractical because they require enormous amounts of computational time. Examples of these types of problems include traffic flow control throughout cities to achieve highly energy-efficient movement, and the structural design of lightweight yet high-strength vehicles and parts. As an example of an application in the mobility field, we have shown that reducing the traffic signal control optimization problem to an energy minimization problem based on the Ising model enables quantum computers to perform high-speed, high-efficiency calculations for signal control of 2,500 signals positioned to simulate a wide-area road network. We engage in initiatives that use quantum computers from the dual approaches of applied research and fundamental algorithm construction in aims of creating the future of manufacturing, and of discovering solutions to the challenges faced by society.

In an effort to realize robotic systems that improve production efficiency, as well as that operate robustly even under complex environments and task commands, we aim to build intelligent technologies that rely on coordination among multiple robots. As one such initiative, we devised a learning algorithm that efficiently shares training data among different types of robots, which has generally been considered a challenge, for the purpose of efficiently applying high volumes of image data obtained from robot groups and sensor networks within facilities. By augmenting the image data collected with three-degrees-offreedom (DoF) mobile robots, we demonstrated that transfer learning and manipulation tasks can be performed when these data are applied as control policy training data for six-DoF robot arms. This effort is expected to significantly reduce the training costs of deep learning as applied to robot control.