Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/109229
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.creator | Zhu, H | - |
dc.creator | Lin, H | - |
dc.creator | Wu, S | - |
dc.creator | Luo, W | - |
dc.creator | Zhang, H | - |
dc.creator | Zhan, Y | - |
dc.creator | Wang, X | - |
dc.creator | Liu, A | - |
dc.creator | Kwek, LC | - |
dc.date.accessioned | 2024-10-03T08:15:07Z | - |
dc.date.available | 2024-10-03T08:15:07Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/109229 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Zhu H, Lin H, Wu S, Luo W, Zhang H, Zhan Y, Wang X, Liu A, Kwek LC. Quantum Computing and Machine Learning on an Integrated Photonics Platform. Information. 2024; 15(2):95 is available at https://doi.org/10.3390/info15020095. | en_US |
dc.subject | Integrated photonic chip | en_US |
dc.subject | Optical network | en_US |
dc.subject | Quantum neural network | en_US |
dc.title | Quantum computing and machine learning on an integrated photonics platform | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 2 | - |
dc.identifier.doi | 10.3390/info15020095 | - |
dcterms.abstract | Integrated photonic chips leverage the recent developments in integrated circuit technology, along with the control and manipulation of light signals, to realize the integration of multiple optical components onto a single chip. By exploiting the power of light, integrated photonic chips offer numerous advantages over traditional optical and electronic systems, including miniaturization, high-speed data processing and improved energy efficiency. In this review, we survey the current status of quantum computation, optical neural networks and the realization of some algorithms on integrated optical chips. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Information (Switzerland), Feb. 2024, v. 15, no. 2, 95 | - |
dcterms.isPartOf | Information (Switzerland) | - |
dcterms.issued | 2024-02 | - |
dc.identifier.scopus | 2-s2.0-85185708452 | - |
dc.identifier.eissn | 2078-2489 | - |
dc.identifier.artn | 95 | - |
dc.description.validate | 202410 bcch | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Others | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Ministry of Education, Singapore; National Research Foundation, Singapore | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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information-15-00095.pdf | 3.09 MB | Adobe PDF | View/Open |
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