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- Publisher Website: 10.1103/PhysRevLett.124.010506
- Scopus: eid_2-s2.0-85078302583
- PMID: 31976736
- WOS: WOS:000505997600003
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Article: Protocol for Implementing Quantum Nonparametric Learning with Trapped Ions
Title | Protocol for Implementing Quantum Nonparametric Learning with Trapped Ions |
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Authors | |
Keywords | Encoding (symbols) Ions Linear transformations Signal encoding Trapped ions |
Issue Date | 2020 |
Publisher | American Physical Society. The Journal's web site is located at https://journals.aps.org/prl/ |
Citation | Physical Review Letters, 2020, v. 124 n. 1, p. 010506:1-010506:7 How to Cite? |
Abstract | Nonparametric learning is able to make reliable predictions by extracting information from similarities between a new set of input data and all samples. Here we point out a quantum paradigm of nonparametric learning that offers an exponential speedup over the sample size. By encoding data into quantum feature space, the similarity between the data is defined as an inner product of quantum states. A quantum training state is introduced to superpose all data of samples, encoding relevant information for learning in its bipartite entanglement spectrum. We demonstrate that a trained state for prediction can be obtained by entanglement spectrum transformation, using the quantum matrix toolbox. We further work out a feasible protocol to implement the quantum nonparametric learning with trapped ions, and demonstrate the power of quantum superposition for machine learning. |
Persistent Identifier | http://hdl.handle.net/10722/280387 |
ISSN | 2023 Impact Factor: 8.1 2023 SCImago Journal Rankings: 3.040 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, D-B | - |
dc.contributor.author | Zhu, S-L | - |
dc.contributor.author | Wang, ZD | - |
dc.date.accessioned | 2020-02-07T07:40:17Z | - |
dc.date.available | 2020-02-07T07:40:17Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Physical Review Letters, 2020, v. 124 n. 1, p. 010506:1-010506:7 | - |
dc.identifier.issn | 0031-9007 | - |
dc.identifier.uri | http://hdl.handle.net/10722/280387 | - |
dc.description.abstract | Nonparametric learning is able to make reliable predictions by extracting information from similarities between a new set of input data and all samples. Here we point out a quantum paradigm of nonparametric learning that offers an exponential speedup over the sample size. By encoding data into quantum feature space, the similarity between the data is defined as an inner product of quantum states. A quantum training state is introduced to superpose all data of samples, encoding relevant information for learning in its bipartite entanglement spectrum. We demonstrate that a trained state for prediction can be obtained by entanglement spectrum transformation, using the quantum matrix toolbox. We further work out a feasible protocol to implement the quantum nonparametric learning with trapped ions, and demonstrate the power of quantum superposition for machine learning. | - |
dc.language | eng | - |
dc.publisher | American Physical Society. The Journal's web site is located at https://journals.aps.org/prl/ | - |
dc.relation.ispartof | Physical Review Letters | - |
dc.rights | Physical Review Letters. Copyright © American Physical Society. | - |
dc.rights | Copyright [2020] by The American Physical Society. This article is available online at [http://dx.doi.org/10.1103/PhysRevLett.124.010506]. | - |
dc.subject | Encoding (symbols) | - |
dc.subject | Ions | - |
dc.subject | Linear transformations | - |
dc.subject | Signal encoding | - |
dc.subject | Trapped ions | - |
dc.title | Protocol for Implementing Quantum Nonparametric Learning with Trapped Ions | - |
dc.type | Article | - |
dc.identifier.email | Wang, ZD: zwang@hku.hk | - |
dc.identifier.authority | Wang, ZD=rp00802 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1103/PhysRevLett.124.010506 | - |
dc.identifier.pmid | 31976736 | - |
dc.identifier.scopus | eid_2-s2.0-85078302583 | - |
dc.identifier.hkuros | 309080 | - |
dc.identifier.volume | 124 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 010506:1 | - |
dc.identifier.epage | 010506:7 | - |
dc.identifier.isi | WOS:000505997600003 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0031-9007 | - |