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Learning temporal data with a variational quantum recurrent neural network
https://repo.qst.go.jp/records/84586
https://repo.qst.go.jp/records/8458601ecb1e0-95b6-4a34-a0aa-0582fa626378
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2021-10-05 | |||||
| タイトル | ||||||
| タイトル | Learning temporal data with a variational quantum recurrent neural network | |||||
| 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 |
Takaki, Yuto
× Takaki, Yuto× Mitarai, Kosuke× Makoto, Negoro× Keisuke, Fujii× Kitagawa, Masahiro× Makoto, Negoro× Keisuke, Fujii |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | We propose a method for learning temporal data using a parametrized quantum circuit. We use the circuit that has a similar structure as the recurrent neural network, which is one of the standard approaches employed for this type of machine learning task. Some of the qubits in the circuit are utilized for memorizing past data, while others are measured and initialized at each time step for obtaining predictions and encoding a new input datum. The proposed approach utilizes the tensor product structure to get nonlinearity with respect to the inputs. Fully controllable, ensemble quantum systems such as an NMR quantum computer are a suitable choice of an experimental platform for this proposal. We demonstrate its capability with simple numerical simulations, in which we test the proposed method for the task of predicting cosine and triangular waves and quantum spin dynamics. Finally, we analyze the dependency of its performance on the interaction strength among the qubits in numerical simulation and find that there is an appropriate range of the strength. This work provides a way to exploit complex quantum dynamics for learning temporal data. | |||||
| 書誌情報 |
PHYSICAL REVIEW A 巻 103, 号 5, p. 052414, 発行日 2021-05 |
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| 出版者 | ||||||
| 出版者 | APS | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 2469-9926 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1103/PhysRevA.103.052414 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://journals.aps.org/pra/abstract/10.1103/PhysRevA.103.052414 | |||||
