Innovative machine learning algorithm driven by violation of detailed balance condition
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- Ohzeki Masayuki
- Principal Investigator
- 東北大学
-
- 一木 輝久
- Co-Investigator
- 名古屋大学
About This Project
- Japan Grant Number
- JP16K13849 (JGN)
- Funding Program
- Grants-in-Aid for Scientific Research
- Funding Organization
- Japan Society for the Promotion of Science
Kakenhi Information
- Project/Area Number
- 16K13849
- Research Category
- Grant-in-Aid for Challenging Exploratory Research
- Allocation Type
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- Multi-year Fund
- Review Section / Research Field
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- Science and Engineering > Mathematics and Physics > Physics > Mathematical physics/Fundamental condensed matter physics
- Research Institution
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- Tohoku University
- Project Period (FY)
- 2016-04-01 〜 2019-03-31
- Project Status
- Completed
- Budget Amount*help
- 3,640,000 Yen (Direct Cost: 2,800,000 Yen Indirect Cost: 840,000 Yen)
Research Abstract
This study is to develop an innovative algorithm in the field of machine learning that uses classical stochastic processes, based on the fact that the convergence to steady state accelerates when the detailed balance is broken. Starting with Physical Review E 93 (2016) 012129 in 2016, we understand the role played by detailed balance as a physical process, go beyond classical stochastic processes, and step into quantum stochastic processes to create diverse algorithms I aimed at. The basic theory for exploiting quantum systems that are difficult to handle due to the presence of the negative sign problem was developed in Scientific Reports, (2017) 41186, and in scientific reports, 8 (2018) 9950, machine learning utilizing quantum fluctuation A demonstration experiment of the algorithm was conducted.
