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Experimental quantum kernel trick with nuclear spins in a solid
https://repo.qst.go.jp/records/84340
https://repo.qst.go.jp/records/843401fca78ac-013f-4c95-bf32-819a67e9ce3f
Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2021-09-29 | |||||
タイトル | ||||||
タイトル | Experimental quantum kernel trick with nuclear spins in a solid | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
アクセス権 | ||||||
アクセス権 | metadata only access | |||||
アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
著者 |
Kusumoto, Takeru
× Kusumoto, Takeru× Mitarai, Kosuke× Fujii, Keisuke× Kitagawa, Masahiro× Makoto, Negoro× Makoto, Negoro |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | The kernel trick allows us to employ high-dimensional feature space for a machine learning task without explicitly storing features. Recently, the idea of utilizing quantum systems for computing kernel functions using interference has been demonstrated experimentally. However, the dimension of feature spaces in those experiments have been smaller than the number of data, which makes them lose their computational advantage over explicit method. Here we show the first experimental demonstration of a quantum kernel machine that achieves a scheme where the dimension of feature space greatly exceeds the number of data using 1H nuclear spins in solid. The use of NMR allows us to obtain the kernel values with single-shot experiment. We employ engineered dynamics correlating 25 spins which is equivalent to using a feature space with a dimension over 1015. This work presents a quantum machine learning using one of the largest quantum systems to date. | |||||
書誌情報 |
npj Quantum Information 巻 7, p. 94, 発行日 2021-06 |
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出版者 | ||||||
出版者 | Nature | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 2056-6387 | |||||
DOI | ||||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1038.s41534-021-00423-0 | |||||
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識別子タイプ | URI | |||||
関連識別子 | https://www.nature.com/articles/s41534-021-00423-0 |