Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network

• Item type: SIG Technical Reports(1)
• 公開日: 2022-06-30
• タイトル: Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network
• タイトル / 言語: en
• タイトル: Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Engineering Science, Osaka University
• 著者所属: Graduate School of Engineering Science, Osaka University／Center for Quantum Information and Quantum Biology, Osaka University／JST, PRESTO
• 著者所属: Graduate School of Engineering Science, Osaka University／Center for Quantum Information and Quantum Biology, Osaka University／RIKEN Center for Quantum Computing
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering Science, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering Science, Osaka University / Center for Quantum Information and Quantum Biology, Osaka University / JST, PRESTO
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering Science, Osaka University / Center for Quantum Information and Quantum Biology, Osaka University / RIKEN Center for Quantum Computing
• 著者名: Yoshiaki, Kawase; Kosuke, Mitarai; Keisuke, Fujii
• 著者名(英): Yoshiaki, Kawase; Kosuke, Mitarai; Keisuke, Fujii
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship, or similarities, between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data. We use fidelity-based metrics instead of Euclidean distance in calculating high-dimensional data similarity. We visualize both classical (Iris dataset) and quantum (time-depending Hamiltonian dynamics) data for classification tasks. Since this method allows us to represent a quantum dataset in a higher dimensional Hilbert space by a quantum dataset in a lower dimension while keeping their similarity, the proposed method can also be used to compress quantum data for further quantum machine learning.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship, or similarities, between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data. We use fidelity-based metrics instead of Euclidean distance in calculating high-dimensional data similarity. We visualize both classical (Iris dataset) and quantum (time-depending Hamiltonian dynamics) data for classification tasks. Since this method allows us to represent a quantum dataset in a higher dimensional Hilbert space by a quantum dataset in a lower dimension while keeping their similarity, the proposed method can also be used to compress quantum data for further quantum machine learning.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12894105
• 書誌情報: 量子ソフトウェア（QS）; 巻 2022-QS-6, 号 2, p. 1-8, 発行日 2022-06-30
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2435-6492
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会