2019
DOI: 10.1103/physreva.100.032111
|View full text |Cite
|
Sign up to set email alerts
|

Macroscopicity of quantum mechanical superposition tests via hypothesis falsification

Abstract: We establish an objective scheme to determine the macroscopicity of quantum superposition tests with mechanical degrees of freedom. It is based on the Bayesian hypothesis falsification of a class of macrorealist modifications of quantum theory, such as the model of Continuous Spontaneous Localization. The measure uses the raw data gathered in an experiment, taking into account all measurement uncertainties, and can be used to directly assess any conceivable quantum mechanical test. We determine the resulting m… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

0
38
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
5
1

Relationship

1
5

Authors

Journals

citations
Cited by 27 publications
(38 citation statements)
references
References 58 publications
0
38
0
Order By: Relevance
“…An experiment can thus be deemed as more macroscopic than another one if its measurement record falsifies a greater set of parameters. This empirical notion of macroscopicity can be cast into a quantitative measure using the concept of Bayesian hypothesis testing [22]. To this end, we consider the odds ratio…”
Section: Empirical Macroscopicitymentioning
confidence: 99%
See 4 more Smart Citations
“…An experiment can thus be deemed as more macroscopic than another one if its measurement record falsifies a greater set of parameters. This empirical notion of macroscopicity can be cast into a quantitative measure using the concept of Bayesian hypothesis testing [22]. To this end, we consider the odds ratio…”
Section: Empirical Macroscopicitymentioning
confidence: 99%
“…Here, H τ * e states that macrorealism holds and a MMM affects (1) with a time parameter τ e τ * e (at fixed σ ); H τ * e assumes weaker modifications, τ e > τ * e , or possibly none at all (τ e → ∞). With help of Bayes' theorem, we can express the odds ratio in terms of the likelihoods P(d|τ e , σ, I ) that the MMM model (1) predicts the observed data d based on the given parameters [22]…”
Section: Empirical Macroscopicitymentioning
confidence: 99%
See 3 more Smart Citations