Home > Publications database > Einstein–Podolsky–Rosen–Bohm experiments: A discrete data driven approach > print |
001 | 1008457 | ||
005 | 20240105070343.0 | ||
024 | 7 | _ | |a 10.1016/j.aop.2023.169314 |2 doi |
024 | 7 | _ | |a 0003-4916 |2 ISSN |
024 | 7 | _ | |a 1096-035X |2 ISSN |
024 | 7 | _ | |a 10.34734/FZJ-2023-02357 |2 datacite_doi |
037 | _ | _ | |a FZJ-2023-02357 |
082 | _ | _ | |a 530 |
100 | 1 | _ | |a De Raedt, Hans |0 P:(DE-Juel1)179169 |b 0 |e Corresponding author |
245 | _ | _ | |a Einstein–Podolsky–Rosen–Bohm experiments: A discrete data driven approach |
260 | _ | _ | |a Amsterdam [u.a.] |c 2023 |b Elsevier |
336 | 7 | _ | |a article |2 DRIVER |
336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1688360261_10248 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a ARTICLE |2 BibTeX |
336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
520 | _ | _ | |a We take the point of view that building a one-way bridge from experimental data to mathematical models instead of the other way around avoids running into controversies resulting from attaching meaning to the symbols used in the latter. In particular, we show that adopting this view offers new perspectives for constructing mathematical models for and interpreting the results of Einstein–Podolsky–Rosen–Bohm experiments. We first prove new Bell-type inequalities constraining the values of the four correlations obtained by performing Einstein–Podolsky–Rosen–Bohm experiments under four different conditions. The proof is “model-free” in the sense that it does not refer to any mathematical model that one imagines to have produced the data. The constraints only depend on the number of quadruples obtained by reshuffling the data in the four data sets without changing the values of the correlations. These new inequalities reduce to model-free versions of the well-known Bell-type inequalities if the maximum fraction of quadruples is equal to one. Being model-free, a violation of the latter by experimental data implies that not all the data in the four data sets can be reshuffled to form quadruples. Furthermore, being model-free inequalities, a violation of the latter by experimental data only implies that any mathematical model assumed to produce this data does not apply. Starting from the data obtained by performing Einstein–Podolsky–Rosen–Bohm experiments, we construct instead of postulate mathematical models that describe the main features of these data. The mathematical framework of plausible reasoning is applied to reproducible and robust data, yielding without using any concept of quantum theory, the expression of the correlation for a system of two spin-1/2 objects in the singlet state. Next, we apply Bell’s theorem to the Stern–Gerlach experiment and demonstrate how the requirement of separability leads to the quantum-theoretical description of the averages and correlations obtained from an Einstein–Podolsky–Rosen–Bohm experiment. We analyze the data of an Einstein–Podolsky–Rosen–Bohm experiment and debunk the popular statement that Einstein–Podolsky–Rosen–Bohm experiments have vindicated quantum theory. We argue that it is not quantum theory but the processing of data from EPRB experiments that should be questioned. We perform Einstein–Podolsky–Rosen–Bohm experiments on a superconducting quantum information processor to show that the event-by-event generation of discrete data can yield results that are in good agreement with the quantum-theoretical description of the Einstein–Podolsky–Rosen–Bohm thought experiment. We demonstrate that a stochastic and a subquantum model can also produce data that are in excellent agreement with the quantum-theoretical description of the Einstein–Podolsky–Rosen–Bohm thought experiment. |
536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
536 | _ | _ | |a OpenSuperQ - An Open Superconducting Quantum Computer (820363) |0 G:(EU-Grant)820363 |c 820363 |f H2020-FETFLAG-2018-03 |x 1 |
588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
700 | 1 | _ | |a Katsnelson, Mikhail I. |0 P:(DE-HGF)0 |b 1 |
700 | 1 | _ | |a Jattana, Manpreet S. |0 P:(DE-Juel1)174485 |b 2 |u fzj |
700 | 1 | _ | |a Mehta, Vrinda |0 P:(DE-Juel1)176997 |b 3 |u fzj |
700 | 1 | _ | |a Willsch, Madita |0 P:(DE-Juel1)167543 |b 4 |u fzj |
700 | 1 | _ | |a Willsch, Dennis |0 P:(DE-Juel1)167542 |b 5 |u fzj |
700 | 1 | _ | |a Michielsen, Kristel |0 P:(DE-Juel1)138295 |b 6 |u fzj |
700 | 1 | _ | |a Jin, Fengping |0 P:(DE-Juel1)144355 |b 7 |u fzj |
773 | _ | _ | |a 10.1016/j.aop.2023.169314 |g Vol. 453, p. 169314 - |0 PERI:(DE-600)1461336-0 |p 169314 |t Annals of physics |v 453 |y 2023 |x 0003-4916 |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/1008457/files/1-s2.0-S0003491623001008-main.pdf |y OpenAccess |
909 | C | O | |o oai:juser.fz-juelich.de:1008457 |p openaire |p open_access |p driver |p VDB |p ec_fundedresources |p openCost |p dnbdelivery |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)179169 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)174485 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)176997 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)167543 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)167542 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)138295 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)144355 |
913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
914 | 1 | _ | |y 2023 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2022-11-08 |
915 | _ | _ | |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0 |0 LIC:(DE-HGF)CCBYNCND4 |2 HGFVOC |
915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2022-11-08 |
915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2023-10-21 |w ger |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2023-10-21 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2023-10-21 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2023-10-21 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1150 |2 StatID |b Current Contents - Physical, Chemical and Earth Sciences |d 2023-10-21 |
915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b ANN PHYS-NEW YORK : 2022 |d 2023-10-21 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-10-21 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2023-10-21 |
915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2023-10-21 |
915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2023-10-21 |
915 | p | c | |a APC keys set |2 APC |0 PC:(DE-HGF)0000 |
915 | p | c | |a Local Funding |2 APC |0 PC:(DE-HGF)0001 |
920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
980 | _ | _ | |a journal |
980 | _ | _ | |a VDB |
980 | _ | _ | |a UNRESTRICTED |
980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
980 | _ | _ | |a APC |
980 | 1 | _ | |a APC |
980 | 1 | _ | |a FullTexts |
Library | Collection | CLSMajor | CLSMinor | Language | Author |
---|