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Probability relations between separated systems

Schrödinger, E.·1936·Mathematical Proceedings of the Cambridge Philosophical Society

Abstract

The paper first scrutinizes thoroughly the variety of compositions which lead to the same quantum-mechanical mixture (as opposed to state or pure state). With respect to a given mixture every state has a definite probability (or mixing fraction) between 0 and 1 (including the limits), which is calculated from the mixtures Statistical Operator and the wave function of the state in question. A well-known example of mixtures occurs when a system consists of two separated parts. If the wave function of the whole system is known, either part is in the situation of a mixture, which is decomposed into definite constituents by a definite measuring programme to be carried out on the other part. All the conceivable decompositions (into linearly independent constituents) of the first system are just realized by all the possible measuring programmes that can be carried out on the second one. In general every state of the first system can be given a finite chance by a suitable choice of the programme. It is suggested that these conclusions, unavoidable within the present theory but repugnant to some physicists including the author, are caused by applying non-relativistic quantum mechanics beyond its legitimate range. An alternative possibility is indicated.

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Metadata

Authors
Schrödinger, E.
Venue
Mathematical Proceedings of the Cambridge Philosophical Society
Published
1936-10
Volume
32
Issue
3
Pages
446-452
ISSN
  • 0305-0041 (ISSN-L)
  • 1469-8064
OpenAlex
W2162753709
Cited by
979
References
0
FWCI
0.78
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Probability relations between separated systems. https://app.cochat.ai/papers/10.1017/s0305004100019137
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Schrödinger, E. (1936). Probability relations between separated systems. Mathematical Proceedings of the Cambridge Philosophical Society. https://doi.org/10.1017/s0305004100019137

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.