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International Journal of Interactive Mobile Technologies 2017年第1期11卷 40-52页

作者： Setyono, A. Haryanto, H. Astuti, E.Z. Department of Informatics Engineering majoring in computer system and data communication Faculty of Computer Science Dian Nuswantoro University Semarang50131 Indonesia Dian Nuswantoro University Semarang India Department of Informatics Engineering majoring in intelligent system Faculty of Computer Science Dian Nuswantoro University Semarang50131 Indonesia

Mobile long-distance web-based monitoring systems will become increasingly common in the near future. This paper focuses on the development of a healthcare telemonitoring system that uses an adaptive web-based framework that monitors several parameters simultaneously. The proposed system simplifies patient and physician access to the healthcare system. It combines many healthcare service functions for a long distance monitoring system. The system immediately sends blood pressure, blood sugar, respiration, heartbeat, pulse, body temperature and transmits them to the clinic's server. All of these parameters can be inputted by patients themselves while the patients are far from the clinical environment and forwarded to the physician for further diagnosis and medical treatment. The advantages of the proposed system are simple patient access through the internet and simple connectivity to a physician from remote areas. A smartphone with multimedia features and an internet connection in a mobile environment can be used to perform mobile computing anywhere and anytime. It keeps real-time recording and monitoring of physiological parameters of patients at a low cost and at home. The physiological parameters of the patient are enhanced by multimedia data. By using the good quality of the multimedia data, the physician can be more precise when providing a diagnosis or medical treatment to the patient. The proposed framework can be used to develop a practical web-based mobile telemonitoring system in a mobile environment.

关键词： Blood pressure

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Alert classification for the alerce broker system: The real-time stamp classifier

arXiv

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arXiv 2020年

作者： Carrasco-Davis, Rodrigo Reyes, E. Valenzuela, C. Förster, F. Estévez, P.A. Pignata, G. Bauer, F.E. Reyes, I. Sánchez-Sáez, P. Cabrera-Vives, G. Eyheramendy, S. Catelan, M. Arredondo, J. Castillo-Navarrete, E. Rodríguez-Mancini, D. Ruz-Mieres, D. Moya, A. Sabatini-Gacitúa, L. Sepúlveda-Cobo, C. Mahabal, A.A. Silva-Farfán, J. Camacho-Iñiguez, E. Galbany, L. Nuncio Monseñor Sótero Sanz 100 Providencia Santiago Chile Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago8320000 Chile Center for Mathematical Modeling Universidad de Chile Beauchef 851 North building 7th floor Santiago8320000 Chile Faculty of Engineering and Sciences Universidad Adolfo Ibañez Diagonal Las Torres 2700 Peñalolén Santiago Chile Data Observatory Santiago Chile Departamento de Astronomía Universidad de Chile Casilla 36D Santiago Chile Departamento de Ciencias Físicas Universidad Andres Bello Av. Republica 230 Santiago8370146 Chile Instituto de Astrofísica and Centro de Astroingeniería Facultad de Física Pontificia Universidad Católica de Chile Casilla 306 Santiago 22 Chile Space Science Institute 4750 Walnut Street Suite 205 BoulderCO80301 United States Inria Chile Research Center Av. Apoquindo 2827 Las Condes Chile Department of Computer Science Universidad de Concepción Edmundo Larenas 219 Concepción Chile Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Cahill Center for Astrophysics California Institute of Technology 1200 E. California Boulevard PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Departamento de Física Teórica y del Cosmos Universidad de Granada GranadaE-18071 Spain

We present a real-time stamp classifier of astronomical events for the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker. The classifier is based on a convolutional neural network, trained on alerts ingested from the Zwicky Transient Facility (ZTF). Using only the science, reference and difference images of the first detection as inputs, along with the metadata of the alert as features, the classifier is able to correctly classify alerts from active galactic nuclei, supernovae (SNe), variable stars, asteroids and bogus classes, with high accuracy (∼94%) in a balanced test set. In order to find and analyze SN candidates selected by our classifier from the ZTF alert stream, we designed and deployed a visualization tool called SN Hunter, where relevant information about each possible SN is displayed for the experts to choose among candidates to report to the Transient Name Server database. From June 26th 2019 to February 28th 2021, we have reported 6846 SN candidates to date (11.8 candidates per day on average), of which 971 have been confirmed spectroscopically. Our ability to report objects using only a single detection means that 70% of the reported SNe occurred within one day after the first detection. ALeRCE has only reported candidates not otherwise detected or selected by other groups, therefore adding new early transients to the bulk of objects available for early follow-up. Our work represents an important milestone toward rapid alert classifications with the next generation of large etendue telescopes, such as the Vera C. Rubin Observatory. Copyright © 2020, The Authors. All rights reserved.

关键词： Supernovae

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Dark Energy Survey Year 3 Results: Mis-centering calibration and X-ray-richness scaling relations in redMaPPer clusters

arXiv

引用

arXiv 2023年

作者： Kelly, P. Jobel, J. Eiger, O. Abd, A. Jeltema, T.E. Giles, P. Hollowood, D.L. Wilkinson, R.D. Turner, D.J. Bhargava, S. Everett, S. Farahi, A. Romer, A.K. Rykoff, E.S. Wang, F. Bocquet, S. Cross, D. Faridjoo, R. Franco, J. Gardner, G. Kwiecien, M. Laubner, D. McDaniel, A. O’Donnell, J.H. Sanchez, L. Schmidt, E. Sripada, S. Swart, A. Upsdell, E. Webber, A. Aguena, M. Allam, S. Alves, O. Bacon, D. Brooks, D. Burke, D.L. Rosell, A. Carnero Carretero, J. Collins, C.A. Costanzi, M. da Costa, L.N. Pereira, M.E.S. Davis, T.M. Doel, P. Ferrero, I. Frieman, J. García-Bellido, J. Giannini, G. Gruen, D. Gruendl, R.A. Hilton, M. Hinton, S.R. Honscheid, K. James, D.J. Kuehn, K. Mann, R.G. Marshall, J.L. Mena-Fernández, J. Miller, C.J. Miquel, R. Myles, J. Palmese, A. Pieres, A. Malagón, A.A. Plazas Rooney, P.J. Sahlen, M. Sanchez, E. Cid, D. Sanchez Schubnell, M. Sevilla-Noarbe, I. Smith, M. Stott, J.P. Suchyta, E. Swanson, M.E.C. Tarle, G. To, C. Viana, P.T.P. Weaverdyck, N. Wiseman, P. University of California Santa Cruz Santa CruzCA95064 United States University of California Davis 1 Shields Ave DavisCA95616 United States Santa Cruz Institute for Particle Physics Santa CruzCA95064 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Swinburne University of Technology John St HawthornVIC3122 Australia Department of Physics and Astronomy Pevensey Building University of Sussex BrightonBN1 9QH United Kingdom 2 place Maurice-Quentin Paris France Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. PasadenaCA91109 United States Departments of Statistics and Data Science University of Texas at Austin AustinTX78757 United States Kavli Institute for Particle Astrophysics & Cosmology Stanford University P. O. Box 2450 StanfordCA94305 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Los Altos High School United States University of California Berkeley University Avenue and Oxford St BerkeleyCA94720 United States University Observatory Faculty of Physics Ludwig-Maximilians-Universität Scheinerstr. 1 Munich81679 Germany Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Homestead High School United States De Anza College 21250 Stevens Creek Blvd CupertinoCA United States Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States California State University San Marcos 333 S Twin Oaks Valley Rd San MarcosCA92096 United States Dougherty Valley High School United States University of New Hampshire 105 Main St DurhamNH03824 United States Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Department of Physics University of Michigan Ann ArborMI48109 United States Institute of Cosmology and G

We use Dark Energy Survey Year 3 (DES Y3) clusters with archival X-ray data from XMM-Newton and Chandra to assess the centering performance of the redMaPPer cluster finder and to measure key richness observable scaling relations. In terms of centering, we find that 10-20% of redMaPPer clusters are miscentered with no significant difference in bins of low versus high richness (20 40) or redshift (0.2 X-λ scatter of 0.21 ± 0.01. While the scatter in TX-λ is consistent in bins of redshift, we do find modestly different slopes with high-redshift clusters displaying a somewhat shallower relation. Splitting based on richness, we find a marginally larger scatter for our lowest richness bin, 20 © 2023, CC BY.

关键词： Galaxies

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Correction to: A new cognitive clock matching phenotypic and epigenetic ages

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Translational psychiatry 2022年第1期12卷 452页

作者： M I Krivonosov E V Kondakova N A Bulanov S A Polevaya C Franceschi M V Ivanchenko M V Vedunova Institute of Biology and Biomedicine Department of Neurotechnology N. I. Lobachevsky State University Nizhny Novgorod Russia. krivonosov@itmm.unn.ru. Institute of Information Technology Mathematics and Mechanics Department of Applied Mathematics N. I. Lobachevsky State University Nizhny Novgorod Russia. krivonosov@itmm.unn.ru. Research Center for Trusted Artificial Intelligence The Ivannikov Institute for System Programming of the Russian Academy of Sciences Moscow 109004 Russia. krivonosov@itmm.unn.ru. Research Center for Trusted Artificial Intelligence The Ivannikov Institute for System Programming of the Russian Academy of Sciences Moscow 109004 Russia. Institute of Biology and Biomedicine Department of Basic and Medical Genetics N. I. Lobachevsky State University Nizhny Novgorod Russia. Faculty of Computer Science School of Data Analysis and Artificial Intelligence HSE University Moscow Russia. Faculty of Social Sciences Department of Psychophysiology N. I. Lobachevsky State University Nizhny Novgorod Russia. Institute of Information Technology Mathematics and Mechanics Department of Applied Mathematics N. I. Lobachevsky State University Nizhny Novgorod Russia. Department of Experimental Diagnostic and Specialty Medicine (DIMES) University of Bologna Bologna Italy.

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The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning

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Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks

TechRxiv

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TechRxiv 2019年

作者： Zhang, Anguo Han, Ying Hu, Jing Niu, Yuzhen Gao, Yueming Chen, Zhizhang Zhao, Kai College of Physics and Information Engineering Fuzhou University Fujian350108 China The Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fuzhou350116 China Research Institute of Ruijie Ruijie Networks Co. Ltd Fujian350002 China School of Public Health Xiamen University Xiamen361102 China College of Information and Intelligent Transportation Fujian Chuanzheng Communications College Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University The Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian350108 China College of Physics and information Engineering Fuzhou University the Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fujian350108 China College of Physics and Information Engineering Fuzhou University 350108 China Faculty of Science and Technology University of Macau 999078 China

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at https://***/anvien/Evol-SNN. © 2019, CC0.

关键词： Neural networks

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge CB10 1SA UK Korea Advanced Institute of Science and Technology Daejeon 34141 South Korea Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Korea Advanced Institute of Science and Technology Daejeon South Korea Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston MA USA Ludwig Center at Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Broad Institute of MIT and Harvard

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Market Basket Analysis Using Apriori and FP-Growth for Analysis Consumer Expenditure Patterns at Berkah Mart in Pekanbaru Riau

Market Basket Analysis Using Apriori and FP-Growth for Analy...

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, computer science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Mustakim Herianda, Della Maulina Ilham, Ahmad Daeng Gs, Achmad Laumal, Folkes E. Kurniasih, Nuning Iskandar, Akbar Manulangga, Gloria Indra Iswara, Ida Bagus Ary Rahim, Robbi Puzzle Research Data Technology UIN Sultan Syarif Kasim Riau Indonesia Department of Information System UIN Sultan Syarif Kasim Riau Indonesia Informatics Department Universitas Muhammadiyah Semarang Semarang Indonesia Department of Management University of 45 Surabaya Surabaya Indonesia Department of Electrical Engineering Politeknik Negeri Kupang Kupang Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Informatics Department STMIK STIKOM Indonesia Denpasar Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia

Berkah Mart is one of the new minimarkets in Pekanbaru city which began to develop as a retail business to meet the needs of the community. In his operation, Berkah Mart still uses the traditional store management perception that the placement of product layout on sale shelves has not been through sales analysis. Existing products have not been reviewed from the consumer's point of view, whereas the marketing economics concept provides a variety of product and consumer management methods for business management in Berkah Mart, including market bucket analysis techniques and various itemset search algorithms. In this research, Market Basket Basket Analysis with FP-Growth algorithm is proposed to determine the layout and planning of goods availability. The application of FP-Growth algorithm proved to be useful in generating many and informative association rules to find out the consumer spending pattern at Berkah Mart in Pekanbaru. Moreover, from the rules of the customer association can be segmented separately to meet the specific needs of customers with cost-effective by using some special promotions for the general group. The experimental results show that FP-Growth algorithm can analyze quickly and efficiently informing consumer shopping pattern at Berkah Mart and can increase Berkah Mart revenue. © Published under licence by IOP Publishing Ltd.

关键词： Sales

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Recurrent Deep Embedding Networks for Genotype Clustering and Ethnicity Prediction

arXiv

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arXiv 2018年

作者： Karim, Md. Rezaul Cochez, Michael Beyan, Oya Deniz Zappa, Achille Sahay, Ratnesh Decker, Stefan Schuhmann, Dietrich-Rebholz Fraunhofer FIT Birlinghoven Sankt Augustin Germany Chair of Computer Science 5 - Information Systems RWTH Aachen University Aachen Germany Insight Centre for Data Analytics National University of Ireland Galway IDA Business Park Dangan Lower Co. Galway Ireland Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

Background: a true understanding of genetic variations assists us in finding correlating population groups, identifying patients who are predisposed to common diseases and solving rare diseases. Albeit, machine learning algorithms have demonstrated tremendous success in this domain over the last decade but relatively less attention is paid in long-term dependencies and representations learning for classifying and clustering large and high-dimensional datasets. On the other hand, deep learning can better exploit such datasets to build networks that model nonlinear relationships among millions of genetic variants. In this paper, we propose an improved variant of Deep Embedding Clustering (DEC) algorithm called Convolutional Deep Embedding Clustering (CDEC) for clustering genetic variants. Recurrent Long-short Term Memory (LSTM) on the other hand, is used for predicting geographic ethnicity from individual’s sample. Results: we used genotype data from the 1000 Genomes project covering genetic variants of 2504 individuals from 26 different ethnic origins, giving 84 million variants to train and evaluate the CDEC and LSTM networks. Experimental results show the efficiency and effectiveness of our approach. In particular, CDEC can cluster targeted population groups in 22 hours with an Adjusted Rand Index (ARI) of 0.915, a Normalized Mutual Information (NMI) of 0.92 and a Clustering Accuracy (ACC) of 0.89. LSTM network on the other hand, can predict the ethnicity of samples from unknown genetic variants with an accuracy of 95.6%, precision of 95%, recall of 93%, F1 of 93% and an RMSE of only 3.15%. Conclusion: experimental results with a focus on accuracy and scalability outperforms state-of-the-art approaches with a high-level of clustering and classification accuracy. Overall, our approach is scalable for 5% to 100% of the full human genetic variants. Copyright © 2018, The Authors. All rights reserved.

关键词： Genome

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Distinguishing unitary gates on the IBM quantum processor

arXiv

引用

arXiv 2018年

作者： Liu, Shusen Li, Yinan Duan, Runyao School of Data and Computer Science Sun Yat-sen University Guangzhou Guangdong510006 China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Centrum Wiskunde & Informatica and Research Center for Quantum Software Netherlands Institute for Quantum Computing Baidu Inc. Beijing100193 China

An unknown unitary gates, which is secretly chosen from several known ones, can always be distinguished perfectly. In this paper, we implement such a task on IBM's quantum processor. More precisely, we experimentally demonstrate the discrimination of two qubit unitary gates, the identity gate and the 2/3π-phase shift gate, using two discrimination schemes - the parallel scheme and the sequential scheme. We program these two schemes on the ibmqx4, a 5-qubit superconducting quantum processor via IBM cloud, with the help of the QSI modules [S. Liu et al., arXiv:1710.09500, 2017]. We report that both discrimination schemes achieve success probabilities at least 85%. Copyright © 2018, The Authors. All rights reserved.

关键词： Program processors

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