Optimizing entanglement manipulation via algebraic–geometric decompositions and semi-definite programming hierarchies
TL;DR
An algebraic–geometric framework is developed that systematically simplifies optimization over separable channels—widely used as approximations of local operations—and strengthens the Doherty–Parrilo–Spedalieri (DPS) hierarchy for solving such problems.
Abstract
In the study of distributed quantum information processing, it is a fundamental problem to optimize local operations in the implementation of non-local quantum operations assisted by limited entanglement. We develop an algebraic–geometric framework that systematically simplifies optimization over separable (SEP) channels—widely used as approximations of local operations—and strengthens the Doherty–Parrilo–Spedalieri (DPS) hierarchy for solving such problems. We apply this framework to computing maximum success probability for exactly implementing a broad range of different non-local operations under SEP channels. First, we present a unified generalization of previous analytical results on the entanglement cost. Via the generalization, we resolve an open problem posed by Yu et al. regarding the entanglement cost of local state discrimination. Second, we numerically determine the trade-off between the strength of entanglement and the success probability of implementing various operations—such as entanglement distillation, non-local unitary channels, measurements, and state verification.