Zobrazeno 1 - 10
of 43 005
pro vyhledávání: '"Gross, D. A."'
Autor:
Annemarie Ambühl
Publikováno v:
Mnemosyne. 68:326-330
Publikováno v:
Quantum 2, 85 (2018)
Randomized benchmarking provides a tool for obtaining precise quantitative estimates of the average error rate of a physical quantum channel. Here we define real randomized benchmarking, which enables a separate determination of the average error rat
Externí odkaz:
http://arxiv.org/abs/1801.06121
Publikováno v:
Accountability in Research: Policies & Quality Assurance; Jul2024, Vol. 31 Issue 5, p497-514, 18p
Autor:
Hannmann, Eckart
Denkmalpflege in Baden-Württemberg – Nachrichtenblatt der Landesdenkmalpflege, Bd. 2, Nr. 1 (1973), Beginn und Erscheinungsfrequenz: Ausgaben pro Band: 4
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e51cfa2fcd5e7f5f674ce864a8cf293c
Publikováno v:
Nature Comm. 8, 15305 (2017)
Well-controlled quantum devices with their increasing system size face a new roadblock hindering further development of quantum technologies: The effort of quantum tomography---the characterization of processes and states within a quantum device---sc
Externí odkaz:
http://arxiv.org/abs/1608.02263
Publikováno v:
Jahrbücher für Geschichte Osteuropas, 1982 Jan 01. 30(3), 477-478.
Externí odkaz:
https://www.jstor.org/stable/41046561
Autor:
Rossiter, Charles E.
Publikováno v:
British Journal of Industrial Medicine, 1985 Jun 01. 42(6), 431-432.
Externí odkaz:
https://www.jstor.org/stable/27723983
Autor:
TERRÉ, F.
Publikováno v:
L'Année sociologique (1940/1948-), 1964 Jan 01. 15, 403-410.
Externí odkaz:
https://www.jstor.org/stable/27886453
Autor:
Hau, T. F.
Publikováno v:
Zeitschrift für Psychosomatische Medizin und Psychoanalyse, 1968 Jan 01. 14(1), 74-74.
Externí odkaz:
https://www.jstor.org/stable/23995466
Publikováno v:
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), pp. 112 - 121, AUAI Press, 2014
One of the goals of probabilistic inference is to decide whether an empirically observed distribution is compatible with a candidate Bayesian network. However, Bayesian networks with hidden variables give rise to highly non-trivial constraints on the
Externí odkaz:
http://arxiv.org/abs/1407.2256