2001
DOI: 10.1088/0305-4470/34/35/327
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Optimal phase estimation and square root measurement

Abstract: We present an optimal strategy having finite outcomes for estimating a single parameter of the displacement operator on an arbitrary finite dimensional system using a finite number of identical samples. Assuming the uniform a priori distribution for the displacement parameter, an optimal strategy can be constructed by making the square root measurement based on uniformly distributed sample points. This type of measurement automatically ensures the global maximality of the figure of merit, that is, the so calle… Show more

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Cited by 6 publications
(9 citation statements)
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“…[13], but we rephrase it here in a slightly different and more practical way according to the results of Ref. [16]. For convenience of calculation we introduce a new basis {|v 0 , |v 1 } for which our great circle of feature states |f (φ) is the equator.…”
Section: B Template Matching By State Estimationmentioning
confidence: 99%
“…[13], but we rephrase it here in a slightly different and more practical way according to the results of Ref. [16]. For convenience of calculation we introduce a new basis {|v 0 , |v 1 } for which our great circle of feature states |f (φ) is the equator.…”
Section: B Template Matching By State Estimationmentioning
confidence: 99%
“…But this cannot happen since M (ĝ) is the optimal maximum-likelihood POVM. Therefore p(ĝ|g) must be maximum inĝ = g. For nonunimodular groups the previous argument does not apply, since the POVM given by (26) is no longer normalized. In fact, the operator ξ ′ does not satisfy the normalization constraints (10), since the DMC operators do not commute with the unitaries U h .…”
Section: Maximum Likelihood Measurementsmentioning
confidence: 98%
“…For example, if the group G is a discrete group of phase shifts, it has been proved in Refs. [25,26,27], that SRM minimize the probability of error in estimating the unknown state, and, equivalently, they maximize the likelihood, i.e. the probability of correct estimation.…”
Section: Maximum Likelihood Measurementsmentioning
confidence: 99%
“…He performs a single square root measurement on | θn . It is shown [16] that such a measurement is optimal to estimate a single parameter θ n , in the sense that a specific score be maximized as mentioned above. We derive POVMs as in Ref.…”
Section: Protocol Via Distillation Of Entanglementmentioning
confidence: 99%
“…[16,17] in order to estimate a single parameter, in the sense that a specific score is maximized. Such a score is something like that (Probability of a situation) × (Fidelity to an ideal situation).…”
Section: Introductionmentioning
confidence: 99%