000916766 001__ 916766
000916766 005__ 20230123101908.0
000916766 037__ $$aFZJ-2023-00090
000916766 1001_ $$0P:(DE-Juel1)191582$$aAi, Jiaqi$$b0$$eCorresponding author$$ufzj
000916766 1112_ $$aJUQCA day$$cJülich$$d2022-11-14 - 2022-11-14$$wGermany
000916766 245__ $$aImprove the Pauli coefficient measurement with Active Learning
000916766 260__ $$c2022
000916766 3367_ $$033$$2EndNote$$aConference Paper
000916766 3367_ $$2DataCite$$aOther
000916766 3367_ $$2BibTeX$$aINPROCEEDINGS
000916766 3367_ $$2DRIVER$$aconferenceObject
000916766 3367_ $$2ORCID$$aLECTURE_SPEECH
000916766 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1672830110_27125$$xInvited
000916766 520__ $$aWe provide an improvement in the process of Active Learning as a concept from machine learning that labels a large amount of data with a small amount of learning material. In this approach, the method is implemented to speed up measuring the Pauli coefficient for the two-qubit gate. The aim of the implementation is to prove the speed up of the measuring process by reducing unwanted interactions.
000916766 536__ $$0G:(DE-HGF)POF4-5224$$a5224 - Quantum Networking (POF4-522)$$cPOF4-522$$fPOF IV$$x0
000916766 909CO $$ooai:juser.fz-juelich.de:916766$$pVDB
000916766 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)191582$$aForschungszentrum Jülich$$b0$$kFZJ
000916766 9131_ $$0G:(DE-HGF)POF4-522$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5224$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vQuantum Computing$$x0
000916766 9141_ $$y2022
000916766 920__ $$lyes
000916766 9201_ $$0I:(DE-Juel1)PGI-2-20110106$$kPGI-2$$lTheoretische Nanoelektronik$$x0
000916766 980__ $$aconf
000916766 980__ $$aVDB
000916766 980__ $$aI:(DE-Juel1)PGI-2-20110106
000916766 980__ $$aUNRESTRICTED