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Mixed Quantum State Dynamics Estimation with Artificial Neural Network

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dc.contributor.authorKim, Changjunko
dc.contributor.authorRhee, June-Koo Kevinko
dc.contributor.authorLee, Woojunko
dc.contributor.authorAhn, Jaewookko
dc.date.accessioned2023-07-04T02:02:31Z-
dc.date.available2023-07-04T02:02:31Z-
dc.date.created2023-06-08-
dc.date.created2023-06-08-
dc.date.issued2018-10-
dc.identifier.citation9th International Conference on Information and Communication Technology Convergence, ICTC 2018, pp.740 - 747-
dc.identifier.urihttp://hdl.handle.net/10203/310244-
dc.description.abstractIn traditional quantum measurements, the size of tomography increases exponentially with the growth of qubit counts. In this paper, we introduce machine learning techniques such as Deep Neural Network (DNN) and Long Short Term Memory (LSTM) to analyze the Quantum Density Matrix up to 4 qubits, with no assumption of governing model.-
dc.languageEnglish-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleMixed Quantum State Dynamics Estimation with Artificial Neural Network-
dc.typeConference-
dc.identifier.wosid000517984800157-
dc.identifier.scopusid2-s2.0-85059461674-
dc.type.rimsCONF-
dc.citation.beginningpage740-
dc.citation.endingpage747-
dc.citation.publicationname9th International Conference on Information and Communication Technology Convergence, ICTC 2018-
dc.identifier.conferencecountryKO-
dc.identifier.conferencelocationJeju Island-
dc.identifier.doi10.1109/ICTC.2018.8539575-
dc.contributor.localauthorRhee, June-Koo Kevin-
dc.contributor.localauthorAhn, Jaewook-
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