| Project/Area Number |
17K18864
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Electrical and electronic engineering and related fields
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| Research Institution | Tohoku University |
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Principal Investigator |
Sato Shigeo 東北大学, 電気通信研究所, 教授 (10282013)
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| Project Period (FY) |
2017-06-30 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2017: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
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| Keywords | 量子計算機 / 脳型計算機 / 量子ヘッブ則 / 超伝導量子ビット / 量子計算 / 学習 / アルゴリズム / 脳型計算 / 神経回路 / 超伝導回路 |
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| Outline of Final Research Achievements |
I studied the realization of a quantum computer which can update the interactions between qubits by learning in order to integrate high-speed parallel processing of quantum computer with automatic algorithm extraction function of brain computer. Based on neuromorphic quantum computation algorithm, I proposed a learning rule by which interactions between qubits can be changed adaptively according to correlations of qubits, and studied the learning ability by theoretical analysis and numerical simulation. Furthermore, I proposed device configuration using superconducting charge qubits for hardware implementation, and showed the effectiveness of the proposed method by numerical simulation in which physical property of qubits is taken into consideration.
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| Academic Significance and Societal Importance of the Research Achievements |
ビッグデータに象徴されるようにICTの発展を背景に、高度情報化がより一層進んでいる現代社会において、莫大な情報を高速に処理しうる量子計算機の必要性が高まっている。量子計算機の開発においてアルゴリズムの整備が喫緊の課題となっており、本研究で提案する学習則は量子計算機にアルゴリズム自動獲得機能を付与するものであり、量子計算機の真の実用化に向けて大きく資するものである。
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