We study the separability problem in mixtures of Dicke states i.e., the separability of the so-called Diagonal Symmetric (DS) states. First, we show that separability in the case of DS in $C^d\otimes C^d$ (symmetric qudits) can be reformulated as a quadratic conic optimization problem. This connection allows us to exchange concepts and ideas between quantum information and this field of mathematics. For instance, copositive matrices can be understood as indecomposable entanglement witnesses for DS states. As a consequence, we show that positivity of the partial transposition (PPT) is sufficient and necessary for separability of DS states for $d \leq 4$. Furthermore, for $d \geq 5$, we provide analytic examples of PPT-entangled states. Second, we develop new sufficient separability conditions beyond the PPT criterion for bipartite DS states. Finally, we focus on $N$-partite DS qubits, where PPT is known to be necessary and sufficient for separability. In this case, we present a family of almost DS states that are PPT with respect to each partition but nevertheless entangled.
Here we focus on symmetric states i.e., those which are invariant under the exchange of their constituents. Symmetric states can be mapped onto spin systems and span solely the largest-spin subspace in the Schur-Weyl duality representation. The Dicke basis (permutationally invariant) provides a very convenient framework to represent the set of symmetric states. An important subset of this set are the diagonal symmetric (DS) states, which correspond to mixtures of Dicke states and, therefore, are diagonal in the Dicke representation.
In our work we have shown that for DS states of two-qudits, i.e. $\rho\in DS( C^d\otimes C^d)$, the problem of separability is completely equivalent to the problem of membership in the set of completely-positive matrices (no to be confused with CP-maps) and the later can be reformulated in terms of a quadratic conic optimization problems. Quadratic conic optimization is an extremely active field of research on mathematics having impact in fields as disparate as economics, genomics or big-data, which provides a way to approach also some important non-convex optimization problems. The equivalence between these two problems has allowed us to import/export ideas between entanglement theory and non-convex quadratic optimization and to provide entanglement witnesses able to detect DS states that are entangled but positive under partial transposition (PPTES). Importantly enough, for such type of states, the entanglement problem is not related to the symmetry, but to the dimension of the constituents. However, it is precisely the symmetry embedded in the DS states which allows to map the problem to a quadratic conic optimization procedure.
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