Taking a theoretical research style in information science, we aim at fostering information processing and statistical mechanics, developing quantum machine learning, and applying quantum computation by utilizing knowledge from mathematical science and physics. In recent years, machine learning, and data science have made remarkable progress. If we take the time to look at the mathematics behind them, rather than just applying them, we often find that the same thing is a transversal concept in different algorithms and different fields. For instance, Neural networks are a concept at the intersection of brain science, machine learning, and statistical mechanics. Computation on a quantum computer, called a quantum random circuit, is organically related to many issues such as quantum mechanics and gravity, the structure of space-time, computational quantity theory, etc., and can be understood through tensor networks. The diffusion model on which generative AI, which has been making remarkable progress recently, is a fusion of non-equilibrium statistical mechanics and machine learning, and is an example of how advanced research in physics matches the needs of the real world.This understanding is the wellspring from which useful, realistic, and new methods can be created in the end. Every field comes down to the same shaped problem in front of science. We develop the insight to find out where the key issues lie. In addition, with this attitude, we study natural science that is connected to society and philosophy that is useful to society. You will write papers, give presentations at conferences, and use your experience and research results to establish startups, collaborate with others, and even collaborate with large corporations in industry and academia.

Wouldn't you like to have an experience like no other?