2014
DOI: 10.1103/physreva.89.014301
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Success probabilities for universal unambiguous discriminators between unknown pure states

Abstract: A universal programmable discriminator can perform the discrimination between two unknown states, and the optimal solution can be approached via the discrimination between the two averages over the uniformly distributed unknown input pure states, which has been widely discussed in previous works. In this paper, we consider the success probabilities of the optimal universal programmable unambiguous discriminators when applied to the pure input states. More precisely, the analytic results of the success probabil… Show more

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Cited by 10 publications
(6 citation statements)
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“…This discriminator can distinguish any pair of states in this device with some probability, unless the two states |ψ 1 and |ψ 2 are identical. The generalizations and the experimental realizations of this programmable discriminator above were also introduced and widely discussed soon [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58]. In these works, either the minimum-error strategy or the unambiguous discrimination strategy was considered, and the two strategies were unified by introducing an error margin [57].…”
Section: Introductionmentioning
confidence: 99%
See 2 more Smart Citations
“…This discriminator can distinguish any pair of states in this device with some probability, unless the two states |ψ 1 and |ψ 2 are identical. The generalizations and the experimental realizations of this programmable discriminator above were also introduced and widely discussed soon [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58]. In these works, either the minimum-error strategy or the unambiguous discrimination strategy was considered, and the two strategies were unified by introducing an error margin [57].…”
Section: Introductionmentioning
confidence: 99%
“…In these works, either the minimum-error strategy or the unambiguous discrimination strategy was considered, and the two strategies were unified by introducing an error margin [57]. The cases for the multiple copies of input states in both program and data registers are discussed, and the optimal solutions are obtained for n A = n C = n, n B = 1 [44,56], for n A = n C = 1, n B = n [41], for n A = n C = n, n B = m [48], and for arbitrary copies of states in both program and data registers [45,55,58], where n A , n B and n C are the copies of states in the registers A, B and C, respectively. The cases for high dimensional (qudit) states in the registers were also considered, and the unambiguous discrimination has been discussed for n A = n B = n C = 1 [42].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…In quantum state identification [1][2][3][4][5][6][7][8][9][10][11][12][13][14] the task is to identify the state of a quantum system, prepared with certain prior probability in a definite state out of a finite set of possible states, where some or all of the states are unknown. The unknown states are encoded into different reference copies, which together have been introduced as the program register in a programmable machine [1] for identifying the state of the probe, carrying the data.…”
Section: Introductionmentioning
confidence: 99%
“…The problem of quantum state identification has been first introduced for the optimal unambiguous identification of two unknown pure qubit states [1]. The in-vestigations have been soon extended to minimum-error identification [2], and generalizations have been also performed to take into account that more than one copy may be available for the probe system and the two reference systems [2][3][4][5][6][7][8][9], including the case that the two unknown states are mixed [7]. For two unknown pure qudit states with Hilbert space dimension d ≥ 2, minimum-error identification has been studied for arbitrary prior probabilities of the states [3], while the maximum overall success probability for unambiguous identification has been obtained in the case when the states are equally probable [3,4].…”
Section: Introductionmentioning
confidence: 99%