QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a m...
Saved in:
| Published in | PloS one Vol. 13; no. 10; p. e0205844 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
17.10.2018
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0205844 |
Cover
| Abstract | Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance.
To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental 'knobs' for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks.
From 200 training sets sampled randomly from the full dataset, we show that the learner's accuracy in recognizing the state of a device is ≈ 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite-a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts. |
|---|---|
| AbstractList | Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental 'knobs' for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. From 200 training sets sampled randomly from the full dataset, we show that the learner's accuracy in recognizing the state of a device is [almost equal to] 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite-a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts. Background Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. Materials and methods To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental 'knobs' for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. Results and discussion From 200 training sets sampled randomly from the full dataset, we show that the learner's accuracy in recognizing the state of a device is [almost equal to] 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite-a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts. Background Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. Materials and methods To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental ‘knobs’ for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. Results and discussion From 200 training sets sampled randomly from the full dataset, we show that the learner’s accuracy in recognizing the state of a device is ≈ 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite—a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts. Background Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. Materials and methods To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental ‘knobs’ for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. Results and discussion From 200 training sets sampled randomly from the full dataset, we show that the learner’s accuracy in recognizing the state of a device is ≈ 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite—a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts. Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance.BACKGROUNDOver the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance.To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental 'knobs' for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks.MATERIALS AND METHODSTo implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental 'knobs' for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks.From 200 training sets sampled randomly from the full dataset, we show that the learner's accuracy in recognizing the state of a device is ≈ 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite-a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts.RESULTS AND DISCUSSIONFrom 200 training sets sampled randomly from the full dataset, we show that the learner's accuracy in recognizing the state of a device is ≈ 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite-a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts. Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental 'knobs' for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. From 200 training sets sampled randomly from the full dataset, we show that the learner's accuracy in recognizing the state of a device is ≈ 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite-a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts. |
| Audience | Academic |
| Author | Wu, Xingyao Ragole, Stephen Zwolak, Justyna P. Taylor, Jacob M. Kalantre, Sandesh S. |
| AuthorAffiliation | 2 National Institute of Standards and Technology, Gaithersburg, MD, 20899, United States of America 4 Joint Quantum Institute, University of Maryland, College Park, MD, 20742, United States of America 3 Department of Physics, Indian Institute of Technology - Bombay, Mumbai, 400076, India 1 Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD, 20742, United States of America University of the Basque Country, SPAIN |
| AuthorAffiliation_xml | – name: 3 Department of Physics, Indian Institute of Technology - Bombay, Mumbai, 400076, India – name: 4 Joint Quantum Institute, University of Maryland, College Park, MD, 20742, United States of America – name: 1 Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD, 20742, United States of America – name: 2 National Institute of Standards and Technology, Gaithersburg, MD, 20899, United States of America – name: University of the Basque Country, SPAIN |
| Author_xml | – sequence: 1 givenname: Justyna P. orcidid: 0000-0002-2286-3208 surname: Zwolak fullname: Zwolak, Justyna P. – sequence: 2 givenname: Sandesh S. surname: Kalantre fullname: Kalantre, Sandesh S. – sequence: 3 givenname: Xingyao surname: Wu fullname: Wu, Xingyao – sequence: 4 givenname: Stephen surname: Ragole fullname: Ragole, Stephen – sequence: 5 givenname: Jacob M. surname: Taylor fullname: Taylor, Jacob M. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30332463$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkl9r2zAUxc3oWNts32BsgsHYHpJZki3bfRiEsm6BQtnfV3EtXycKiuRa8tp--ylNWpJSWPGDzfHvHt17j46TA-ssJslrmk4oL-inpRt6C2bSRXmSsjQvs-xZckQrzsaCpfxg5_swOfZ-maY5L4V4kRzylHOWCX6UyO9nxl0RowOSBgJ4DCdkSlagFtri2CD0Vts5ga7rXRRJcCQskKgF9HMkPkBAT7QllwPYMKxI4wLB6w57vUIb_MvkeQvG46vte5T8Pvvy6_Tb-Pzi6-x0ej5WomJhLEpkRQPQiJargkFa1mkU6rpF5NjwCjDj0BSsEUApLVCVqmxYXdYsT2mp-Ch5u_HtjPNyuxsvGWU0Kxiv0kjMNkTjYCm72B_0N9KBlreC6-cS-qCVQUlZCVmRK1pTlVU5lMBLiCYCRVtnNUavfOM12A5ursCYe0OaynU8dy3IdTxyG0-s-7ztcqhX2Ki4oR7MXjP7f6xeyLn7KwWtmIiBjZIPW4PeXQ7og1xpr9AYsOiG23lZXgmas4i-e4A-vpUtNYc4uLati-eqtamc5munQmRravIIFZ8GV1rFCVsd9b2Cj3sFkQl4HeYweC9nP388nb34s8--32EXCCYsvDND0M76ffDN7qbvV3x38yNwsgFU77zvsZVKx8scfeJo2vwvx-xB8ZPi_we3yivC |
| CitedBy_id | crossref_primary_10_1080_0951192X_2023_2294441 crossref_primary_10_1103_PhysRevApplied_19_054077 crossref_primary_10_1103_PhysRevApplied_23_014072 crossref_primary_10_21468_SciPostPhysCodeb_35 crossref_primary_10_1007_s42979_021_00921_0 crossref_primary_10_1103_PhysRevApplied_13_034075 crossref_primary_10_1038_s41534_021_00434_x crossref_primary_10_1103_PhysRevApplied_17_024069 crossref_primary_10_1109_ACCESS_2024_3387024 crossref_primary_10_1088_2632_2153_ac104c crossref_primary_10_1103_PhysRevApplied_20_034067 crossref_primary_10_1063_5_0215622 crossref_primary_10_1103_PhysRevApplied_13_054005 crossref_primary_10_1103_PRXQuantum_2_020335 crossref_primary_10_1103_RevModPhys_95_011006 crossref_primary_10_1038_s41534_024_00878_x crossref_primary_10_1103_PhysRevApplied_11_024063 crossref_primary_10_1088_1751_8121_ab0195 crossref_primary_10_1103_PhysRevB_110_205428 crossref_primary_10_1016_j_mtnano_2023_100345 crossref_primary_10_1088_2632_2153_ac34db crossref_primary_10_1109_TQE_2024_3445967 crossref_primary_10_21468_SciPostPhys_13_4_084 crossref_primary_10_1088_2632_2153_ad88d5 |
| Cites_doi | 10.1103/PhysRevA.69.062320 10.1186/1759-4499-2-1 10.1103/PhysRevA.57.120 10.1038/npjqi.2016.34 10.1038/nature16961 10.3115/1072064.1072067 10.1038/s41534-016-0004-0 10.1103/RevModPhys.79.1217 10.1088/1751-8113/41/6/065304 10.1038/nature08812 10.1063/1.5025928 10.1088/0953-4075/49/20/202001 10.1038/s41467-018-03251-7 10.1098/rsif.2014.1289 10.1126/sciadv.1601540 10.1038/nature24622 10.1126/science.aaa2501 10.1126/science.aao4309 10.1063/1.5031034 10.1109/ICRC.2016.7738703 10.1038/529445a 10.1088/1464-4266/7/7/001 10.1063/1.4952624 10.1038/nature00784 10.1126/sciadv.aar3960 10.1038/379413a0 10.1103/PhysRevApplied.6.054013 10.1103/PhysRevB.95.235305 10.1103/PhysRevApplied.10.054026 10.1007/978-1-4899-0415-7 10.1103/RevModPhys.75.1 10.1038/srep34226 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2018 Public Library of Science This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2018 Public Library of Science – notice: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QG 7QL 7QO 7RV 7SN 7SS 7T5 7TG 7TM 7U9 7X2 7X7 7XB 88E 8AO 8C1 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS ATCPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU D1I DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. KB. KB0 KL. L6V LK8 M0K M0S M1P M7N M7P M7S NAPCQ P5Z P62 P64 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PTHSS PYCSY RC3 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1371/journal.pone.0205844 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Opposing Viewpoints Gale In Context: Science ProQuest Central (Corporate) Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Nursing & Allied Health Database Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Meteorological & Geoastrophysical Abstracts Nucleic Acids Abstracts Virology and AIDS Abstracts Agricultural Science Collection Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Public Health Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Journals Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Materials Science Collection ProQuest Central Korea Engineering Research Database Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Materials Science Database Nursing & Allied Health Database (Alumni Edition) Meteorological & Geoastrophysical Abstracts - Academic ProQuest Engineering Collection Biological Sciences Agriculture Science Database ProQuest Health & Medical Collection Medical Database Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Engineering Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Environmental Science Database Materials Science Collection ProQuest Central Premium ProQuest One Academic ProQuest Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition Engineering Collection Environmental Science Collection Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Meteorological & Geoastrophysical Abstracts Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database Virology and AIDS Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Environmental Science Collection Entomology Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Materials Science Collection ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection AIDS and Cancer Research Abstracts Materials Science Database ProQuest Materials Science Collection ProQuest Public Health ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Animal Behavior Abstracts Materials Science & Engineering Collection Immunology Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Agricultural Science Database MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 5 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) Computer Science |
| DocumentTitleAlternate | Machine learning with the QFlow lite dataset |
| EISSN | 1932-6203 |
| ExternalDocumentID | 2121472390 oai_doaj_org_article_128a475c1b1c495a8a38a2396e6fb4be 10.1371/journal.pone.0205844 PMC6192646 A559617640 30332463 10_1371_journal_pone_0205844 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GeographicLocations | United States--US |
| GeographicLocations_xml | – name: United States--US |
| GrantInformation_xml | – fundername: ; |
| GroupedDBID | --- 123 29O 2WC 53G 5VS 7RV 7X2 7X7 7XC 88E 8AO 8C1 8CJ 8FE 8FG 8FH 8FI 8FJ A8Z AAFWJ AAUCC AAWOE AAYXX ABDBF ABIVO ABJCF ABUWG ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV ADRAZ AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHMBA ALMA_UNASSIGNED_HOLDINGS AOIJS APEBS ARAPS ATCPS BAWUL BBNVY BCNDV BENPR BGLVJ BHPHI BKEYQ BPHCQ BVXVI BWKFM CCPQU CITATION CS3 D1I D1J D1K DIK DU5 E3Z EAP EAS EBD EMOBN ESTFP ESX EX3 F5P FPL FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IEA IGS IHR IHW INH INR IOV IPY ISE ISR ITC K6- KB. KQ8 L6V LK5 LK8 M0K M1P M48 M7P M7R M7S M~E NAPCQ O5R O5S OK1 OVT P2P P62 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PUEGO PV9 PYCSY RNS RPM RZL SV3 TR2 UKHRP WOQ WOW ~02 ~KM 3V. ALIPV BBORY CGR CUY CVF ECM EIF IPNFZ NPM RIG 7QG 7QL 7QO 7SN 7SS 7T5 7TG 7TM 7U9 7XB 8FD 8FK AZQEC C1K DWQXO FR3 GNUQQ H94 K9. KL. M7N P64 PKEHL PQEST PQUKI RC3 7X8 5PM ADTOC UNPAY - 02 AAPBV ABPTK ADACO BBAFP KM |
| ID | FETCH-LOGICAL-c692t-68e27daad6f3c72a08b027dbbfee3ed39ae43ad72d6a1117ec8c8d2b8b25018c3 |
| IEDL.DBID | M48 |
| ISSN | 1932-6203 |
| IngestDate | Fri Nov 26 17:13:09 EST 2021 Fri Oct 03 12:52:50 EDT 2025 Sun Oct 26 03:11:19 EDT 2025 Tue Sep 30 15:14:40 EDT 2025 Sat Sep 27 21:08:39 EDT 2025 Tue Oct 07 07:46:51 EDT 2025 Mon Oct 20 21:58:55 EDT 2025 Mon Oct 20 16:12:28 EDT 2025 Thu Oct 16 14:59:49 EDT 2025 Thu Oct 16 15:08:22 EDT 2025 Thu May 22 21:20:41 EDT 2025 Wed Feb 19 02:37:02 EST 2025 Thu Apr 24 22:55:56 EDT 2025 Wed Oct 01 03:46:56 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Language | English |
| License | This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. cc-by Creative Commons CC0 public domain |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c692t-68e27daad6f3c72a08b027dbbfee3ed39ae43ad72d6a1117ec8c8d2b8b25018c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. |
| ORCID | 0000-0002-2286-3208 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://doi.org/10.1371/journal.pone.0205844 |
| PMID | 30332463 |
| PQID | 2121472390 |
| PQPubID | 1436336 |
| PageCount | e0205844 |
| ParticipantIDs | plos_journals_2121472390 doaj_primary_oai_doaj_org_article_128a475c1b1c495a8a38a2396e6fb4be unpaywall_primary_10_1371_journal_pone_0205844 pubmedcentral_primary_oai_pubmedcentral_nih_gov_6192646 proquest_miscellaneous_2122596152 proquest_journals_2121472390 gale_infotracmisc_A559617640 gale_infotracacademiconefile_A559617640 gale_incontextgauss_ISR_A559617640 gale_incontextgauss_IOV_A559617640 gale_healthsolutions_A559617640 pubmed_primary_30332463 crossref_citationtrail_10_1371_journal_pone_0205844 crossref_primary_10_1371_journal_pone_0205844 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2018-10-17 |
| PublicationDateYYYYMMDD | 2018-10-17 |
| PublicationDate_xml | – month: 10 year: 2018 text: 2018-10-17 day: 17 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Francisco – name: San Francisco, CA USA |
| PublicationTitle | PloS one |
| PublicationTitleAlternate | PLoS One |
| PublicationYear | 2018 |
| Publisher | Public Library of Science Public Library of Science (PLoS) |
| Publisher_xml | – name: Public Library of Science – name: Public Library of Science (PLoS) |
| References | ref12 YY Liu (ref48) 2015; 347 TE Oliphant (ref28) 2006 ref18 B Lekitsch (ref5) 2017; 3 MA Nielsen (ref2) 2011 T Müller (ref51) 2018; 9 R Li (ref8) 2018; 4 A Sparkes (ref43) 2010; 2 CJ van Diepen (ref19) 2018; 113 A Blais (ref13) 2004; 69 DJ Griffiths (ref26) 1999 ref42 TD Ladd (ref1) 2010; 464 DM Zajac (ref7) 2016; 6 ref41 JM Gambetta (ref3) 2017; 3 U Mukhopadhyay (ref6) 2018; 112 K Manouchehri (ref46) 2008; 41 D Loss (ref49) 1998; 57 KR Brown (ref14) 2016; 2 H Bernien (ref15) 2017; 551 ref40 DC Unitt (ref50) 2005; 7 ref35 ref34 RC Ashoori (ref45) 1996; 379 M Saffman (ref11) 2016; 49 ref31 ref30 ref33 ref32 C Neill (ref10) 2018; 360 D Silver (ref37) 2016; 529 ref39 ref38 WG van der Wiel (ref21) 2003; 75 ref23 K Williams (ref44) 2015; 12 ref25 ref20 ref22 D Kielpinski (ref4) 2002; 417 AA Melnikov (ref47) 2016; 6 S Lundqvist (ref24) 1983 ref27 ref29 TA Baart (ref17) 2016; 108 E Gibney (ref36) 2016; 529 T Karzig (ref9) 2017; 95 R Hanson (ref16) 2007; 79 |
| References_xml | – volume: 69 start-page: 1 issue: 6 year: 2004 ident: ref13 article-title: Cavity quantum electrodynamics for superconducting electrical circuits: An architecture for quantum computation publication-title: Phys Rev A doi: 10.1103/PhysRevA.69.062320 – volume: 2 start-page: 1 year: 2010 ident: ref43 article-title: Towards Robot Scientists for autonomous scientific discovery publication-title: Autom Exp doi: 10.1186/1759-4499-2-1 – volume: 57 start-page: 120 issue: 1 year: 1998 ident: ref49 article-title: Quantum computation with quantum dots publication-title: Phys Rev A doi: 10.1103/PhysRevA.57.120 – ident: ref20 – volume: 2 start-page: 16034 year: 2016 ident: ref14 article-title: Co-Designing a Scalable Quantum Computer with Trapped Atomic Ions publication-title: npj Quantum Information doi: 10.1038/npjqi.2016.34 – year: 1999 ident: ref26 article-title: Introduction to electrodynamics – ident: ref27 – volume: 529 start-page: 484 year: 2016 ident: ref37 article-title: Mastering the game of Go with deep neural networks and tree search publication-title: Nature doi: 10.1038/nature16961 – ident: ref31 doi: 10.3115/1072064.1072067 – volume: 3 start-page: 2 year: 2017 ident: ref3 article-title: Building logical qubits in a superconducting quantum computing system publication-title: npj Quantum Information doi: 10.1038/s41534-016-0004-0 – volume: 79 start-page: 1217 year: 2007 ident: ref16 article-title: Spins in few-electron quantum dots publication-title: Rev Mod Phys doi: 10.1103/RevModPhys.79.1217 – ident: ref34 – volume: 41 start-page: 065304 year: 2008 ident: ref46 article-title: Quantum walks in an array of quantum dots publication-title: J Phys A: Math Theor doi: 10.1088/1751-8113/41/6/065304 – ident: ref30 – volume: 464 start-page: 45 year: 2010 ident: ref1 article-title: Quantum computers publication-title: Nature doi: 10.1038/nature08812 – year: 2006 ident: ref28 article-title: Guide to NumPy – volume: 112 start-page: 183505 issue: 18 year: 2018 ident: ref6 article-title: A 2 × 2 quantum dot array with controllable inter-dot tunnel couplings publication-title: Appl Phys Lett doi: 10.1063/1.5025928 – volume: 49 start-page: 202001 year: 2016 ident: ref11 article-title: Quantum computing with atomic qubits and Rydberg interactions: Progress and challenges publication-title: J Phys B: At Mol Opt Phys doi: 10.1088/0953-4075/49/20/202001 – ident: ref40 – ident: ref23 – volume: 9 start-page: 862 year: 2018 ident: ref51 article-title: A quantum light-emitting diode for the standard telecom window around 1,550 nm publication-title: Nat Commun doi: 10.1038/s41467-018-03251-7 – volume: 12 start-page: 20141289 year: 2015 ident: ref44 article-title: Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases publication-title: J R Soc Interface doi: 10.1098/rsif.2014.1289 – ident: ref33 – ident: ref39 – volume: 3 start-page: e1601540 issue: 2 year: 2017 ident: ref5 article-title: Blueprint for a microwave trapped ion quantum computer publication-title: Sci Adv doi: 10.1126/sciadv.1601540 – volume: 551 start-page: 579 year: 2017 ident: ref15 article-title: Probing many-body dynamics on a 51-atom quantum simulator publication-title: Nature doi: 10.1038/nature24622 – ident: ref29 – ident: ref41 – volume: 347 start-page: 285 issue: 6219 year: 2015 ident: ref48 article-title: Semiconductor double quantum dot micromaser publication-title: Science doi: 10.1126/science.aaa2501 – volume: 360 start-page: 195 issue: 6385 year: 2018 ident: ref10 article-title: A blueprint for demonstrating quantum supremacy with superconducting qubits publication-title: Science doi: 10.1126/science.aao4309 – ident: ref22 – ident: ref25 – ident: ref32 – volume: 113 start-page: 033101 year: 2018 ident: ref19 article-title: Automated tuning of inter-dot tunnel coupling in double quantum dots publication-title: Appl Phys Lett doi: 10.1063/1.5031034 – year: 2011 ident: ref2 article-title: Quantum Computation and Quantum Information – ident: ref12 doi: 10.1109/ICRC.2016.7738703 – volume: 529 start-page: 445 year: 2016 ident: ref36 article-title: Google AI algorithm masters ancient game of Go publication-title: Nature doi: 10.1038/529445a – volume: 7 start-page: S129 year: 2005 ident: ref50 article-title: Quantum dots as single-photon sources for quantum information processing publication-title: J. Opt. B: Quantum Semiclass. Opt doi: 10.1088/1464-4266/7/7/001 – ident: ref38 – volume: 108 start-page: 1 issue: 21 year: 2016 ident: ref17 article-title: Computer-automated tuning of semiconductor double quantum dots into the single-electron regime publication-title: Appl Phys Lett doi: 10.1063/1.4952624 – volume: 417 start-page: 709 year: 2002 ident: ref4 article-title: Architecture for a large-scale ion-trap quantum computer publication-title: Nature doi: 10.1038/nature00784 – volume: 4 start-page: eaar3960 issue: 7 year: 2018 ident: ref8 article-title: A crossbar network for silicon quantum dot qubits publication-title: Sci Adv doi: 10.1126/sciadv.aar3960 – volume: 379 start-page: 413 year: 1996 ident: ref45 article-title: Electrons in artificial atoms publication-title: Nature doi: 10.1038/379413a0 – volume: 6 start-page: 054013 year: 2016 ident: ref7 article-title: Scalable Gate Architecture for a One-Dimensional Array of Semiconductor Spin Qubits publication-title: Phys Rev Appl doi: 10.1103/PhysRevApplied.6.054013 – volume: 95 start-page: 1 issue: 23 year: 2017 ident: ref9 article-title: Scalable designs for quasiparticle-poisoning-protected topological quantum computation with Majorana zero modes publication-title: Phys Rev B doi: 10.1103/PhysRevB.95.235305 – ident: ref18 doi: 10.1103/PhysRevApplied.10.054026 – ident: ref42 – year: 1983 ident: ref24 article-title: Theory of the Inhomogeneous Electron Gas doi: 10.1007/978-1-4899-0415-7 – volume: 75 start-page: 1 year: 2003 ident: ref21 article-title: Electron transport through double quantum dots publication-title: Rev Mod Phys doi: 10.1103/RevModPhys.75.1 – ident: ref35 – volume: 6 start-page: 34226 year: 2016 ident: ref47 article-title: Quantum walks of interacting fermions on a cycle graph publication-title: Sci Rep doi: 10.1038/srep34226 |
| SSID | ssj0053866 |
| Score | 2.4896827 |
| Snippet | Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their... Background Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting... BACKGROUND:Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting... Background Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting... |
| SourceID | plos doaj unpaywall pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e0205844 |
| SubjectTerms | Algorithms Arrays Artificial intelligence Artificial neural networks Automation Biology and Life Sciences Cancer Collaboration Computation Computational Biology - methods Computer and Information Sciences Computer science Computer Security Computer Simulation Computers Conductance Cybersecurity Data Mining Data processing Databases, Factual Drug Discovery Earth Sciences Engineering and Technology Information processing Internet Knobs Learning algorithms Machine Learning Methods Neural networks Neural Networks (Computer) Optimization Physical Sciences Problem solving Programming Languages Quantum computers Quantum computing Quantum Dots Quantum theory Reproducibility of Results Research and Analysis Methods Resistance Scholarly publishing Semiconductors Software packages Training Tuning |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELbQXuCCKK-mFDAICThku3G8dsJtQawKEiAeRb1ZfqVUSpNtk6ji3zN2nNCISu2B63piyTPj8TfamW8QeiEzbixNkthwbWNKFItzmts4t5ITq6gq_GzAT5_Z_gH9eLg8vDDqy9WE9fTAveL2IH5Kypc6UYkGMC8zmWaSpDmzrICtrIu-iywfkqk-BsMtZiw0yqU82Qt2mW_qys4BIMGrSycPkefrH6PybFPWzWWQ89_KyZtdtZG_z2VZXniW1nfQ7YAn8ao_xxa6Yau7aCvc2Aa_CrTSr-8h8XVd1ufY9RxjVxfa2PYNXuETX01p4zA-4ggPLOO4rTGgQ-y5lCz2nUcNPq7waQfW6E4w5LP474CA5j46WL__8W4_DuMVYs1y0sYss4QbKQ0rUs2JXGQKclSjVGFtak2aS0tTaTgxTEJE5FZnOjNEZYo4FkCdPkCzChS6jbAjJZSSUsOZpIwyaXJNQDIvNCAkSiKUDroWOnCPuxEYpfB_qHHIQXp1CWchESwUoXj8atNzb1wh_9aZcZR1zNn-B_AnEfxJXOVPEXrqnED0bajj_RcrSL3gLIwuIvTcSzj2jMqV5xzJrmnEhy8_ryH0_dtE6GUQKmpQh5ahJQLO5Fi5JpK7E0mIAXqyvO1cdtBKIwCQJJTDsdyXgxtfvvxsXHabupK7ytadlyFu_yVY72Hv9aNmAfcAEGdphPjkPkxUP12pjn958nKXsIOHRGg-3pxrGXfnfxj3EboFgNfzGSd8F83as84-BlDZqic-fvwBFmJ2pA priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwELdG9wAvwMbHAgMMQgIe0jWOaydICHVo1UCiwGDT3izHdsqkLOmWRhP_PWfXyYiYYG9RfInk-_Jdcvc7hF7KhGtDoyjUXJmQkoyFKU1NmBrJiclolrvZgJ9nbP-QfjoeH6-hWdsLY8sqW5_oHLWulP1GvgMuNqKcQIr-fnEW2qlR9u9qO0JD-tEK-p2DGLuB1i39aIDWd_dmXw9a3wzWzZhvoIt5tOPlNVxUpRlC4ASnMe0dUA7Hv_PWg0VR1VeFon9XVN5syoX8dSGL4o_janoX3fZxJp6sFGMDrZlyE91pZzhgb9KbaMNf1fi1R6B-cw-Jb9OiusC2PRnbEtLaLN_iCT51hZcm9JMm5rgFJMfLCkMgiR3sksGuSanGJyU-a0BwzSmG1BdfzhKo76PD6d6PD_uhn8QQKpaSZcgSQ7iWUrM8VpzIUZJBOquzLDcmNjpOpaGx1JxoJsF5cqMSlWiSJRmxgIEqfoAGJfB4C2GLXyglpZozSRllUqeKAGWaKwimKAlQ3LJfKA9TbqdlFML9e-OQrqw4KKzQhBdagMLuqcUKpuM_9LtWsh2tBdl2N6rzufA2K-DolpSPVZRFCvJImcg4kaCBzLActNgE6JnVC7HqWO1chZhAlgZ7YXQUoBeOwgJtlLaSZy6buhYfvxxdg-j7QY_olSfKK2CHkr57AvZkAbx6lNs9SnAXqre8ZbW45UotLg0Lnmw1--rl592yfamtzitN1TgaYt8_Buk9XBlCx1kIkSBmZ3GAeM9Eeqzvr5QnPx3Ouc3tQUMCNOyM6VrCffTvfTxGtyDqdaDGEd9Gg-V5Y55AZLnMnnp38Rtub3rH priority: 102 providerName: ProQuest – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELbK9gAXoLwaKGAQEiCRZWN77YTbglgVpJZnq3KKHNspFdtkIYkq-PWMHSc0UMRyjceRZsZjfyPPfEbogYyFNiyKQi2UCRnJeJiwxISJkYKYjGW5extwZ5dv77HXB9ODNfSk64U5fX9PRfTUW3S8LAszBmgD5yU7h9b5FJD3CK3v7b6dfWovjknIyYT67ri_TR2cPo6kv9-KR8tFWZ2FM_8slzzfFEv5_UQuFqfOovkltNNp0ZagfBk3dTZWP34jeFxVzcvoogeleNauog20ZooraMOHfYUfeW7qx1dR-m6-KE-wbVzGtri0MvUzPMPHriTThP4NikPcUZXjusQAMbEjZDLYtS9V-KjAXxtwaXOMISnGv14ZqK6hvfnLjy-2Q_9GQ6h4QuqQx4YILaXmOVWCyEmcQaKrsyw3hhpNE2kYlVoQzSVsq8KoWMWaZHFGLJWgotfRqAClNxG2zIZSMqYFl4wzLnWiCEgmuQKYxUiAaOe7VHkCc_uOxiJ1t3ICEpnWXKm1YuqtGKCwn7VsCTz-If_cLote1tJvuw_grtRHcwqHumRiqqIsUpBhyljSWBKacMNzWN8mQHftokrbXtZ-E0lnkL-BLpxNAnTfSVgKjsLW-BzKpqrSV2_2VxD68H4g9NAL5SWYQ0nfVwE6WWqvgeTWQBI2EjUY3rQh0FmlSgHVREyAWnZmFxZnD9_rh-1Pbd1eYcrGyRD7_yl470YbRb1lATwBmuc0QGIQXwPTD0eKo8-OAd1m_bBCAjTuI3El59783wm30AVAyI4AORJbaFR_a8xtQKF1dsdvPj8BW2qHAg priority: 102 providerName: Unpaywall |
| Title | QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/30332463 https://www.proquest.com/docview/2121472390 https://www.proquest.com/docview/2122596152 https://pubmed.ncbi.nlm.nih.gov/PMC6192646 https://doi.org/10.1371/journal.pone.0205844 https://doaj.org/article/128a475c1b1c495a8a38a2396e6fb4be http://dx.doi.org/10.1371/journal.pone.0205844 |
| UnpaywallVersion | publishedVersion |
| Volume | 13 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVFSB databaseName: Free Full-Text Journals in Chemistry customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: HH5 dateStart: 20060101 isFulltext: true titleUrlDefault: http://abc-chemistry.org/ providerName: ABC ChemistRy – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: KQ8 dateStart: 20060101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: KQ8 dateStart: 20061001 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: DOA dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: ABDBF dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: EBSCOhost Food Science Source customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: A8Z dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/login.aspx?authtype=ip,uid&profile=ehost&defaultdb=fsr providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: DIK dateStart: 20060101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: GX1 dateStart: 20060101 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M~E dateStart: 20060101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: RPM dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7X7 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: BENPR dateStart: 20061201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 8FG dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 8C1 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/publichealth providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal Journals: Open Access customDbUrl: eissn: 1932-6203 dateEnd: 20250930 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M48 dateStart: 20061201 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwELe2ToK9IMbXAqMYhAQ8pFoc104mIdRNKwNpZQyKylPkOE6ZlCVd02jbf8_ZdQIRndhLHuJzFN-X75Lz7xB6LQKeKOp5bsKlcimJmRvSULmhEpyomMap6Q14PGJHY_p50p-sobpnq2VguTK10_2kxvOsd3Vx_QEM_r3p2sC9elJvVuSqB-EP7Kl0HW3AXhXqZg7HtPmvANbNmD1Ad9PMTXQH3DrEGcxv7VUG0r9x3J1ZVpSrotJ_iyvvVvlMXF-KLPtr5xreR_dsyIkHSx3ZQmsqf4C2rFGX-K1Fnn73EEVfh1lxifWxZKxLR0u12MMDfG4KLpVrO0xMcQ1EjhcFhgASG7glhc3hpBKf5fiiAoFV5xhSXvynh0D5CI2Hh98PjlzbgcGVLCQLlwWK8ESIhKW-5ETsBjGksUkcp0r5KvFDoagvEk4SJsBpciUDGSQkDmKigQKl_xh1cuDtNsIat1AIShPOBGWUiSSUBCjDVEIQRYmD_JrXkbTw5LpLRhaZf24c0pQluyItrMgKy0FuM2u2hOf4D_2-FmNDq8G1zY1iPo2srUawZQvK-9KLPQn5owiEHwjih0yxFLRXOeiFVoJoeVK1cRHRALIzWAujuw56ZSg0wEauK3imoirL6NOXH7cg-nbaInpjidIC2CGFPTUBa9LAXS3KnRYluAnZGt7WKltzpYwgZvEoh2XpmbUarx5-2Qzrh-qqvFwVlaEh-vl9kN6TpdY3nK1tyEG8ZQ8t1rdH8rNfBt9c5_SgIQ7qNZZzK-E-vfElnqFNCHQNjrHHd1BnMa_UcwgmF3EXrfMJh2tw4Onr8GMXbewfjk5Ou-bzTNf4D7g3Hp0Mfv4GjmF6dg |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELdGeRgvwMbHAoMZBAIe0i2OaydICJWPamUfCNhQ34zjOGVSl3RLo2r_FH8jZ9fJiJhgL3ur6osl35fvkrvfIfRMRjzVNAj8lCvtU5IwP6ax9mMtOdEJTTI7G3Bvn20f0k-j3mgJ_ap7YUxZZe0TraNOC2XekW-Ciw0oJ5Civ52e-GZqlPm6Wo_QWKjFjj6bQ8pWvhl-APk-J2Tw8eD9tu-mCviKxWTms0gTnkqZsixUnMitKIHULE2STOtQp2EsNQ1lyknKJDgCrlWkopQkUUIM-J0KYd9r6DoNwZeA_fBRk-CB72DMteeFPNh02tCdFrnuQlgGdz1tXX92SkBzF3Smk6K8KND9u15zucqn8mwuJ5M_LsPBbXTTRbG4v1C7FbSk81V0q54QgZ3DWEUr7leJXzp861d3kPgymBRzbJqfsSlQLfXsNe7jY1vWqX03x2KMa7hzPCswhKnYgjppbFugSnyU45MK1KI6xpBY4_NJBeVddHglErmHOjnweA1hg44oJaUpZ5IyymQaKwKUcaYgVKPEQ2HNfqEcCLqZxTER9sseh2RowUFhhCac0DzkN09NFyAg_6F_ZyTb0BoIb_tHcToWziMICAwk5T0VJIGCLFVGMowk6DfTLAMb0R7aMHohFv2wjSMSfcgB4SyMbnnoqaUwMB65qRMay6osxfDz90sQffvaInrhiLIC2KGk682AMxl4sBbleosSnJFqLa8ZLa65Uopzs4Una82-ePlJs2w2NbV_uS4qS0PM_j2Q3v2FITSchQAMMgIWeoi3TKTF-vZKfvTToqibNwegIR7qNsZ0KeE--Pc5NtDy9sHertgd7u88RDcgvrbwyQFfR53ZaaUfQQw7Sx5bx4HRj6v2VL8B98CzBg |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELdGkYAXYONjhcEMAgEPaRfHtRMkhAqj2hiM8THUN-M4TpnUJd3SqNq_xl_H2XEyIibYy96q-mLJ9-W75O53CD2RIU809X0v4Up7lMTMi2ikvUhLTnRM49TOBvy4y7b26fvxYLyEftW9MKassvaJ1lEnuTLvyPvgYn3KCaTo_dSVRextjl7PjjwzQcp8aa3HaVQqsqNPFpC-Fa-2N0HWTwkZvfv2dstzEwY8xSIy91ioCU-kTFgaKE7kRhhDmpbEcap1oJMgkpoGMuEkYRKcAtcqVGFC4jAmBghPBbDvJXSZB0Fkygn5uEn2wI8w5lr1Au73nWb0ZnmmexCiwb1PW1ehnRjQ3Aud2TQvzgp6_67dvFpmM3mykNPpHxfj6Ca67iJaPKxUcBkt6WwF3ainRWDnPFbQsvtV4OcO6_rFLSQ-j6b5AptGaGyKVQs9f4mH-NCWeGrPzbSY4Br6HM9zDCErtgBPGtt2qAIfZPioBBUpDzEk2fh0akFxG-1fiETuoE4GPF5F2CAlSklpwpmkjDKZRIoAZZQqCNso6aKgZr9QDhDdzOWYCvuVj0NiVHFQGKEJJ7Qu8pqnZhUgyH_o3xjJNrQGztv-kR9PhPMOAoIESflA-bGvIGOVoQxCCbrONEvBXnQXrRu9EFVvbOOUxBDyQTgLoxtd9NhSGEiPzBjHRJZFIbY_fT8H0dcvLaJnjijNgR1Kuj4NOJOBCmtRrrUowTGp1vKq0eKaK4U4NWF4stbss5cfNctmU1MHmOm8tDTE7D8A6d2tDKHhLARjkB2woIt4y0RarG-vZAc_LaK6eYsAGtJFvcaYziXce_8-xzq6Aj5KfNje3bmPrkGobZGUfb6GOvPjUj-AcHYeP7R-A6MfF-2ofgOHF7dJ |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELbK9gAXoLwaKGAQEiCRZWN77YTbglgVpJZnq3KKHNspFdtkIYkq-PWMHSc0UMRyjceRZsZjfyPPfEbogYyFNiyKQi2UCRnJeJiwxISJkYKYjGW5extwZ5dv77HXB9ODNfSk64U5fX9PRfTUW3S8LAszBmgD5yU7h9b5FJD3CK3v7b6dfWovjknIyYT67ri_TR2cPo6kv9-KR8tFWZ2FM_8slzzfFEv5_UQuFqfOovkltNNp0ZagfBk3dTZWP34jeFxVzcvoogeleNauog20ZooraMOHfYUfeW7qx1dR-m6-KE-wbVzGtri0MvUzPMPHriTThP4NikPcUZXjusQAMbEjZDLYtS9V-KjAXxtwaXOMISnGv14ZqK6hvfnLjy-2Q_9GQ6h4QuqQx4YILaXmOVWCyEmcQaKrsyw3hhpNE2kYlVoQzSVsq8KoWMWaZHFGLJWgotfRqAClNxG2zIZSMqYFl4wzLnWiCEgmuQKYxUiAaOe7VHkCc_uOxiJ1t3ICEpnWXKm1YuqtGKCwn7VsCTz-If_cLote1tJvuw_grtRHcwqHumRiqqIsUpBhyljSWBKacMNzWN8mQHftokrbXtZ-E0lnkL-BLpxNAnTfSVgKjsLW-BzKpqrSV2_2VxD68H4g9NAL5SWYQ0nfVwE6WWqvgeTWQBI2EjUY3rQh0FmlSgHVREyAWnZmFxZnD9_rh-1Pbd1eYcrGyRD7_yl470YbRb1lATwBmuc0QGIQXwPTD0eKo8-OAd1m_bBCAjTuI3El59783wm30AVAyI4AORJbaFR_a8xtQKF1dsdvPj8BW2qHAg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=QFlow+lite+dataset%3A+A+machine-learning+approach+to+the+charge+states+in+quantum+dot+experiments&rft.jtitle=PloS+one&rft.au=Zwolak%2C+Justyna+P&rft.au=Kalantre%2C+Sandesh+S&rft.au=Wu%2C+Xingyao&rft.au=Ragole%2C+Stephen&rft.date=2018-10-17&rft.eissn=1932-6203&rft.volume=13&rft.issue=10&rft.spage=e0205844&rft_id=info:doi/10.1371%2Fjournal.pone.0205844&rft_id=info%3Apmid%2F30332463&rft.externalDocID=30332463 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-6203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-6203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-6203&client=summon |