Question Answering on Quantum Computers.
The first quantum algorithm for processing language was run in 2020, sparking great research interest in the field. This article introduces the background of quantum computation as well as the relationship between quantum theory and the theory of natural language, intending to make the topic accessi...
| Publicado en: | Journal of the Utah Academy of Sciences, Arts & Letters Vol. 100; pp. 321 - 333 |
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| Formato: | Artículo |
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Utah Academy of Sciences, Arts & Letters
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=176893924&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 176893924 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: B0JA jtl: Journal of the Utah Academy of Sciences, Arts & Letters maglogo: N pubinfo: dt: 2023 vid: 100 pid: 59066 pub: Utah Academy of Sciences, Arts & Letters artinfo: ui: 176893924 ppf: 321 ppct: 12 formats: fmt: @attributes: type: P size: 3MB tig: atl: Question Answering on Quantum Computers. aug: au: Draper, Thomas affil: Brigham Young University su: Linguistics Quantum computers Natural languages Prediction models Parameter estimation sug: subj: Linguistics Quantum computers Natural languages Prediction models Parameter estimation ab: The first quantum algorithm for processing language was run in 2020, sparking great research interest in the field. This article introduces the background of quantum computation as well as the relationship between quantum theory and the theory of natural language, intending to make the topic accessible to an audience familiar with linear algebra. No knowledge of linguistics or quantum computing is assumed. This paper illustrates the method of converting sentences to quantum circuits. The algorithm is implemented in Python to parse sentences and convert them to parameterized quantum circuits. IBM's Qiskit framework is used to build these circuits and evaluate them using simulators and actual quantum hardware. The circuit's parameters are optimized using a gradient-free machine learning method, which allows accurate predictions for the truth of simple sentences. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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