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Tight bounds on the distinguishability of quantum states under separable measurements. (2013)

by S Bandyopadhyay, M Nathanson
Venue:Physical Review A,
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Limitations on separable measurements by convex optimization

by Somshubhro Bandyopadhyay , Alessandro Cosentino , Nathaniel Johnston , Vincent Russo , John Watrous , Nengkun Yu - Information Theory, IEEE Transactions on , 2015
"... The paradigm of local operations and classical communication, or LOCC for short, is fundamental within the theory of quantum information. A protocol involving two or more individuals is said to be an LOCC protocol when it may be implemented by means of classical communication among the individuals, ..."
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The paradigm of local operations and classical communication, or LOCC for short, is fundamental within the theory of quantum information. A protocol involving two or more individuals is said to be an LOCC protocol when it may be implemented by means of classical communication among the individuals, along with arbitrary quantum operations performed locally. Within the LOCC paradigm, the problem of discrimination of quantum states (bipartite or multipartite) has been extensively studied. In the most typically considered variant of this problem, the two parties are given a single copy of a quantum bipartite state chosen with some probability from a known collection of states and their goal is to identify which state was given by means of an LOCC measurement. Many examples are known of specific choices of pure, orthogonal states for which a perfect discrimination is not possible through LOCC measurements. Some of these examples, along with other general results concerning this problem, may be found in [BW09, BDF + 99, DFXY09, Fan04, GKR + 01, GKRS04, HMM + 06, HSSH03, Nat05, WH02, WSHV00, Wat05, YDY12, YDY14]. As perhaps the simplest example of an instance of this problem where a perfect LOCC discrimination is not possible, one has that the four standard Bell states cannot be perfectly discriminated by LOCC measurements [GKR + 01]. In particular, if the states are selected with uniform probability, it holds that the maximum probability of distinguishing them via LOCC is 1/2. Among the other known examples of collections of orthogonal pure states that cannot be perfectly discriminated by LOCC protocols, the so-called domino state example of [BDF + 99] is noteworthy. The particular relevance of this example lies in the fact that all of these states are product states, demonstrating that entanglement is not a requisite for a set of orthogonal pure states to fail to be perfectly discriminated by any LOCC measurement. The set of measurements that can be implemented through LOCC has an apparently complex mathematical structure-no tractable characterization of this set is known, representing a clear obstacle to a better understanding of the limitations of LOCC measurements. For this reason, the state discrimination problem described above is sometimes considered for more tractable classes of measurements that approximate, in some sense, the LOCC measurements. The classes of positive-partial-transpose (PPT) and separable measurements represent two commonly studied
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...M and positive semidefinite operators Q1, . . . , QM ∈ Pos(X ) and R1, . . . , RM ∈ Pos(Y); and a measurement {P1, . . . , PN} on X ⊗ Y is said to be a separable measurement if it is the case that each measurement operator Pk is separable. It is straightforward to prove that every LOCC measurement is a separable measurement and every separable measurement is in turn a PPT measurement. It follows that any limitation proved to hold for every separable or PPT measurement also hold for every LOCC measurement. The state discrimination problem for separable measurements has been investigated before [DFXY09, BN13], although many natural questions remain still open. In this work, we apply techniques from convex optimization in order to answer several of these questions. A nice mathematical property of the set of separable measurements is the fact that an optimization over this set can be cast in the framework of cone programming, which is a generalization of linear programming and semidefinite programming that allows for optimizations over general closed, convex cones. From a computational point of view, the characterization of the problem as a cone program does not seem to help, as optimizing over the ...

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