2015
DOI: 10.1063/1.4934235
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Minimal sufficient positive-operator valued measure on a separable Hilbert space

Abstract: We introduce a concept of a minimal sufficient positive-operator valued measure (POVM), which is the least redundant POVM among the POVMs that have the equivalent information about the measured quantum system. Assuming the system Hilbert space to be separable, we show that for a given POVM a sufficient statistic called a Lehmann-Scheffé-Bahadur statistic induces a minimal sufficient POVM. We also show that every POVM has an equivalent minimal sufficient POVM and that such a minimal sufficient POVM is unique up… Show more

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Cited by 19 publications
(32 citation statements)
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“…9 Recently, the author has introduced the concept of the minimal sufficient POVM, which is the least redundant POVM among the POVMs that bring us the same information about the measured quantum system. 10 In Ref. 10 it is shown that any POVM on a separable Hilbert space is postprocessing equivalent to a minimal sufficient POVM unique up to almost isomorphism.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…9 Recently, the author has introduced the concept of the minimal sufficient POVM, which is the least redundant POVM among the POVMs that bring us the same information about the measured quantum system. 10 In Ref. 10 it is shown that any POVM on a separable Hilbert space is postprocessing equivalent to a minimal sufficient POVM unique up to almost isomorphism.…”
Section: Introductionmentioning
confidence: 99%
“…10 In Ref. 10 it is shown that any POVM on a separable Hilbert space is postprocessing equivalent to a minimal sufficient POVM unique up to almost isomorphism. Then it is natural to ask whether we can generalize the notion of minimal sufficiency to noncommutative statistical experiments and whether we can establish existence and uniqueness up to isomorphism for such general statistical experiments as in the case of POVM.…”
Section: Introductionmentioning
confidence: 99%
“…An observable A with an outcome set Ω is said to be pairwise linearly independent if any pair (A(x 1 ), A(x 2 )), x 1 , x 2 ∈ Ω; x 1 = x 2 , is linearly independent. Every observable is post-processing equivalent to a pairwise linearly independent observable unique up to the permutation of the outcome set [11]. An observable A is pairwise linearly independent if and only if A is minimal sufficient, that is, for any Markov kernel p ∈ Markov(Ω, Ω) the condition p * A = A implies p(x, x ) = δ x,x [11].…”
Section: Appendix B Pairwise Linearly Independent Observablesmentioning
confidence: 99%
“…Every observable is post-processing equivalent to a pairwise linearly independent observable unique up to the permutation of the outcome set [11]. An observable A is pairwise linearly independent if and only if A is minimal sufficient, that is, for any Markov kernel p ∈ Markov(Ω, Ω) the condition p * A = A implies p(x, x ) = δ x,x [11]. We recall that two Markov kernels p ∈ Markov(Ω 1 , Ω 2 ) and q ∈ Markov(Ω 2 , Ω 3 ) can be combined into a new Markov kernel as follows:…”
Section: Appendix B Pairwise Linearly Independent Observablesmentioning
confidence: 99%
“…i.e., the relabeling mediated by f takes B into A B. In statistical terms, this means that this coarse-graining (statistic) is sufficient for B and, according to [14,Prop. 5], there is a function h :…”
Section: Define the Observable B : ωmentioning
confidence: 99%