000858209 001__ 858209
000858209 005__ 20210129235900.0
000858209 0247_ $$2Handle$$a2128/20337
000858209 037__ $$aFZJ-2018-07115
000858209 041__ $$aEnglish
000858209 1001_ $$0P:(DE-Juel1)167543$$aWillsch, Madita$$b0$$eCorresponding author$$ufzj
000858209 1112_ $$aBad Honnef Physics School on Quantum Technologies$$cBad Honnef$$d2018-08-05 - 2018-08-10$$wGermany
000858209 245__ $$aSuperconducting flux qubits compared to ideal two-level systems as building blocks for quantum annealers
000858209 260__ $$c2018
000858209 3367_ $$033$$2EndNote$$aConference Paper
000858209 3367_ $$2BibTeX$$aINPROCEEDINGS
000858209 3367_ $$2DRIVER$$aconferenceObject
000858209 3367_ $$2ORCID$$aCONFERENCE_POSTER
000858209 3367_ $$2DataCite$$aOutput Types/Conference Poster
000858209 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1544102905_16463$$xOther
000858209 520__ $$aQuantum annealers provide a promising approach for solving optimization problems.The theory of quantum annealing is fundamentally different from gate-based quantum computing: In quantum annealing, the system is prepared in a known ground state of an initial Hamiltonian, then this Hamiltonian is adiabatically transformed into the final Hamiltonian whose ground state corresponds to the solution of the given problem.Quantum annealing works well in theory if the qubits can be modeled as two-level systems. However, in real devices, the qubits are not based on perfect two-level systems, but on a two-dimensional subspace of a larger system. This makes approximations in analytic calculations unavoidable.With a simulation utilizing the Suzuki-Trotter product-formula approach to solve the time-dependent Schrödinger equation, the time-evolution of the full state of such a device based on superconducting flux qubits is investigated and compared to the ideal two-level system.
000858209 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000858209 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x1
000858209 7001_ $$0P:(DE-Juel1)167542$$aWillsch, Dennis$$b1$$ufzj
000858209 7001_ $$0P:(DE-Juel1)144355$$aJin, Fengping$$b2$$ufzj
000858209 7001_ $$0P:(DE-HGF)0$$aDe Raedt, Hans$$b3
000858209 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b4$$ufzj
000858209 8564_ $$uhttps://juser.fz-juelich.de/record/858209/files/bad_honnef.pdf$$yOpenAccess
000858209 8564_ $$uhttps://juser.fz-juelich.de/record/858209/files/bad_honnef.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000858209 909CO $$ooai:juser.fz-juelich.de:858209$$pdriver$$pVDB$$popen_access$$popenaire
000858209 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)167543$$aForschungszentrum Jülich$$b0$$kFZJ
000858209 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)167542$$aForschungszentrum Jülich$$b1$$kFZJ
000858209 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144355$$aForschungszentrum Jülich$$b2$$kFZJ
000858209 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138295$$aForschungszentrum Jülich$$b4$$kFZJ
000858209 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000858209 9141_ $$y2018
000858209 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000858209 920__ $$lyes
000858209 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000858209 980__ $$aposter
000858209 980__ $$aVDB
000858209 980__ $$aUNRESTRICTED
000858209 980__ $$aI:(DE-Juel1)JSC-20090406
000858209 9801_ $$aFullTexts