Application of quantum annealing for data analysis
| Project/Area Number |
17K14357
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Mathematical physics/Fundamental condensed matter physics
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| Research Institution | Tokyo University of Science |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 量子アニーリング / データ解析 / 材料・デバイス応用 / 熱場ダイナミクス / 状態変化の評価 / 主成分分析 / 判断分析 / エントロピー / 時間短縮 / アルゴリズム / ビッグデータ |
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| Outline of Final Research Achievements |
Quantum annealing is an efficient method to solve optimization problems by using quantum physics. In the present study, we have investigated the usage of quantum annealing without limit to optimization problems. And we clarified two important applications. One is an application to clarify the details of the thermoelectric materials. We show that spectral conductivity can be estimated by observable parameters, namely electrical conductivity and Seebeck coefficients, using quantum annealing. This is an inverse problem of integral equations. Another is applications to sensor networks. Sensors set on buildings give us a lot of information. We have to extract important parts of the information. We tried this extraction by quantum annealing with supplementally using deep learning methods. Furthermore, for giving the wide application of the quantum annealing for data analysis, and efficient time schedule is required. We found that entropy plays a sufficient role to find the time schedule.
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| Academic Significance and Societal Importance of the Research Achievements |
世界中のいたるところをセンサーが結ぶIoT（Internet of Things）社会が実現した際には，集積された莫大なデータを解析することが必要になる．大規模なデータを適切に処理し，有益な情報を手に入れるためには，データ処理にむけた技術が必要とされる．本研究ではこの要求に応えることを目指して，情報統計力学の生んだ強力な手法である量子アニーリングをより一般的かつ多面的に大規模データの解析へと活用できる，新しい例を示すことができた．具体的な材料やデバイスへの応用可能性が示され，今後の様々な応用につながるといえる．
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Report
(4 results)
Research Products
(33 results)
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[Presentation] Machine Learning Classification Methods Using Data of 3-Axis Acceleration Sensors Equipped with Wireless Communication Means for Locating Wooden House Structural Damage2019
Author(s)
Ryota Tanida, Atsushi Yamamoto, Noriaki Takahashi, Natsuhiko Sakiyama, Sakuya Kishi, Takayuki Kishimoto, So Hasegawa, Kenjiro Mori, Yoichiro Hashizume, Jing Ma, Takashi Nakajima, Mikio Hasegawa, Takahiro Yamamoto, Takumi Ito, Takayuki Kawahara
Organizer
IEEE 15th of the annual Asia Pacific Conference on Circuits and Systems (APCCAS 2019)
Related Report
Int'l Joint Research
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