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Mathematical Informatics Lab
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A mathematical informatics lab that explores the theory of information science using ideas from physics.
AboutAbout the lab
Always doing something new and a little unconventional
The Mathematical Informatics Lab studies the theory of information science using ideas from the mathematical sciences and physics.
Using topics such as quantum annealing and machine learning, it aims to establish statistical mechanics of information, advance quantum machine learning, and apply quantum computing.
The lab also works with startups and carries out industry-academia collaboration with large companies.
Lab introduction videos and lecture archives are available on YouTube.
Key terms
- Quantum annealing
- A computational method that uses a physical mechanism to efficiently find good solutions among a huge number of combinations
- Statistical mechanics of information
- A field that analyzes algorithms and theory in information science using methods derived from statistical mechanics in physics
- Sparse modeling
- A method that assumes only a few factors are truly important and extracts the essentials from a small amount of data
ResearchResearch themes
The Mathematical Informatics Lab takes a theoretical approach within information science, drawing on insights from the mathematical sciences and physics. It aims to establish statistical mechanics of information, advance quantum machine learning, and apply quantum computing, and it also pursues industry-academia collaboration with companies.
*Illustrative imageQuantum Annealing
The lab studies technology that uses physical mechanisms to search efficiently for good combinations. It is advancing industrialization together with Sigma-i Co., Ltd. and Jij Inc., and also conducts industry-academia collaboration with many other companies.
*Illustrative imageStatistical Mechanics of Information
The lab provides solid guarantees for algorithms and theory in information science and derives new methods from unconventional perspectives. Using methods that originate in physics, it provides theoretical guarantees for handling large-scale, high-dimensional data.
*Illustrative imageMachine Learning and Quantum Machine Learning
The lab views machine learning as defining physical laws inside a computer and training it by giving data as boundary conditions. It also looks ahead to a new framework of machine learning for an era in which quantum computers are in ordinary use.
*Illustrative imageSparse Modeling
If only a few factors truly matter, limited data can be used to the fullest. The lab studies formulation and modeling based on sparsity, which is effective when little data is available, and is advancing applications together with Hacarus Inc.
MembersFaculty
The members of the Mathematical Informatics Lab include faculty, visiting researchers, research fellows, administrative assistants, and doctoral, master's, and undergraduate students.
- ProfessorMasayuki Ohzeki
- ProfessorJiahe Ou
- Project Associate ProfessorYuki Sughiyama
- Project Associate ProfessorRumiko Honda
- Assistant ProfessorManaka Okuyama
- Assistant ProfessorMasaru Hitomi
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Wire Bonded Superconducting Quantum Chip with 6 Transmons 001” OJB Quantum / CC BY 4.0 (cropped)
- Quantum Annealing: “D-Wave-Washington-1000Q” Mwjohnson0 / CC BY-SA 4.0 (cropped)
- Statistical Mechanics of Information: “Liquid Crystal Phase Transition” KarlGaff / CC BY 4.0 (cropped)
- Machine Learning and Quantum Machine Learning: “Data flock (digits) by Philipp Schmitt” Philipp Schmitt / CC BY-SA 4.0 (cropped)
- Sparse Modeling: “CADD workstation and operator” United States Department of Transportation - Federal Highway Administration / Public domain (cropped)
- Related lab (大規模データ数理最適化講座): “Network graph visualization showing bibliography and references” Ecpp / CC BY-SA 4.0 (cropped)
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- Related lab (高性能計算論): “Pleiades supercomputer racks 4” Oleg Alexandrov / CC BY-SA 4.0 (cropped)
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