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Physical Fluctuomatics Lab
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A lab investigating the essence of machine learning while also advancing quantum computing techniques.
AboutAbout the lab
Opening up intelligent computation through physics and information processing.
The Physical Fluctuomatics Lab conducts research with the goal of making machine learning a technology that anyone can use.
Machine learning is a technology in which computers read the relationships behind data, learn the underlying laws, and make predictions about the future.
The lab works on research to understand the essence of deep learning, and on advancing computational techniques such as sparse modeling and quantum annealing.
Through many projects and joint research with companies, the lab aims to return its research results to society.
Key terms
- Machine learning
- A technology in which computers read the relationships behind data, learn the underlying laws, and make predictions about the future
- Sparse modeling
- An information processing technique that identifies the essential parts of data even from limited information
- Quantum annealing
- A leading example of a computational technique that uses physical processes to efficiently solve optimization problems
ResearchResearch themes
The lab's goal is to make machine learning, in which computers learn underlying laws from data, a technology that anyone can use. It works on research to understand the essence of learning, sparse modeling, which identifies what is essential from limited information, and computational techniques represented by quantum annealing.
*Illustrative imageUnderstanding the Essence of Deep Learning
Deep learning has become able to carry out highly advanced tasks automatically. The lab conducts bold, exploratory research to understand why it can do so, and what the essence of learning is.
*Illustrative imageCapturing the Essence from Limited Data
In reality, large amounts of high-quality data cannot be collected. The lab advances sparse modeling, which identifies the essential parts from limited information, to maximize the efficiency of experiments and measurements.
*Illustrative imageQuantum Annealing and Optimization Problems
To efficiently solve optimization problems that arise in a wide variety of settings, the lab advances computational techniques that use physical processes, represented by quantum annealing.
MembersFaculty
- ProfessorKazuyuki Tanaka
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 002” OJB Quantum / CC BY 4.0 (cropped)
- Understanding the Essence of Deep Learning: “Artificial Neural Network with Chip” mikemacmarketing / original posted on flickr Liam Huang / clipped and posted on flickr / CC BY 2.0 (cropped)
- Capturing the Essence from Limited Data: “Compressed Sensing” Jeff b02901013 / CC BY-SA 4.0 (cropped)
- Quantum Annealing and Optimization Problems: “Frank-Wolfe Algorithm” Stephanie Stutz / CC BY 4.0 (cropped)
- Related lab (Embodied Intelligence Robotics Lab): “Otonaroid® (adult android), a teleoperated android robot that lets you talk to other through an android, exhibited at Miraikan ( by Franklin Heijnen)” Franklin Heijnen / CC BY-SA 2.0 (cropped)
- Related lab (Advanced Applied Data Science Lab): “Transmission electron microscope (2)” Gannu03 / CC BY-SA 4.0 (cropped)
- Related lab (AI-Applied Communication Technology Lab): “Singtel-dishes” mailer_diablo / CC BY-SA 3.0 (cropped)
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