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Physical Review A

Set discrimination of quantum states

Shengyu Zhang, Mingsheng Ying

Published Jun 19, 2002• 35 citations
Publisher

Abstract

We introduce a notion of set discrimination, which is an interesting extension of quantum state discrimination. A state is secretly chosen from a number of quantum states, which are partitioned into some disjoint sets. A set discrimination is required to identify which set the given state belongs to. Several essential problems are addressed in this paper, including the condition of perfect set discrimination, unambiguous set discrimination, and in the latter case, the efficiency of the discrimination. This generalizes some important results on quantum state discrimination in the literature. A combination of state and set discrimination and the efficiency are also studied.

DOI
10.1103/physreva.65.062322

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We introduce a notion of set discrimination, which is an interesting extension of quantum state discrimination. A state is secretly chosen from a number of quantum states, which are partitioned into some disjoint sets. A set discrimination is required to identify which set the given state belongs to. Several essential problems are addressed in this paper, including the condition of perfect set discrimination, unambiguous set discrimination, and in the latter case, the efficiency of the discrimination. This generalizes some important results on quantum state discrimination in the literature. A combination of state and set discrimination and the efficiency are also studied.
We address the problem of unambiguous discrimination between a given set of quantum operations. The necessary and sufficient condition for them to be unambiguously distinguishable is derived in the cases of single use and multiple uses respectively. For the latter case we explicitly construct the input states and corresponding measurements that accomplish the task. It is found that the introduction of entanglement can improve the discrimination. The advantage of our strategy over minimum-error discrimination is also discussed.
Deterministic discrimination of nonorthogonal states is forbidden by quantum measurement theory. However, if we do not want to succeed all the time, i.e., allow for inconclusive outcomes to occur, then unambiguous discrimination becomes possible with a certain probability of success. A variant of the problem is set discrimination: the states are grouped in sets and we want to determine to which particular set a given pure input state belongs. We consider here the simplest case, termed quantum state filtering, when the NNN given nonorthogonal states ${\ensuremath{\mid}{\ensuremath{\psi}}_{1}⟩,\dots{},\ensuremath{\mid}{\ensuremath{\psi}}_{N}⟩}$ are divided into two sets and the first set consists of one state only while the second consists of all of the remaining states. We present the derivation of the optimal measurement strategy, in terms of a generalized measurement (positive-operator-valued measure), to distinguish $\ensuremath{\mid}{\ensuremath{\psi}}_{1}⟩$ from the set ${\ensuremath{\mid}{\ensuremath{\psi}}_{2}⟩,\dots{},\ensuremath{\mid}{\ensuremath{\psi}}_{N}⟩}$ and the corresponding optimal success and failure probabilities. The results, but not the complete derivation, were presented previously [Phys. Rev. Lett. 90, 25901 (2003)] as the emphasis there was on appplication of the results to probabilistic quantum algorithms. We also show that the problem is equivalent to the discrimination of a pure state and an arbitrary mixed state.
In quantum state filtering one wants to determine whether an unknown quantum state, which is chosen from a known set of states, [|psi(1)>, em leader,|psi(N)>], is either a specific state, say |psi(1)>, or one of the remaining states, [|psi(2)>, em leader,|psi(N)>]. We present the optimal solution to this problem, in terms of generalized measurements, for the case that the filtering is required to be unambiguous. As an application, we propose an efficient, probabilistic quantum algorithm for distinguishing between sets of Boolean functions, which is a generalization of the Deutsch-Jozsa algorithm.