Semidefinite programming hierarchies for constrained bilinear optimization
Mario Berta, Francesco Borderi, Omar Fawzi, Volkher Scholz

TL;DR
This paper develops converging semidefinite programming hierarchies for constrained bilinear optimization, with applications to quantum error correction, and introduces new quantum de Finetti theorems to analyze convergence and approximation quality.
Contribution
It introduces novel semidefinite programming hierarchies for bilinear programs and quantum channels, with convergence guarantees and improved de Finetti theorems for quantum information.
Findings
Hierarchies provide outer bounds on bilinear programs with quantum constraints
Finite de Finetti theorems quantify closeness to product channels and classical communication
Improved approximation factors for de Finetti theorems without symmetry
Abstract
We give asymptotically converging semidefinite programming hierarchies of outer bounds on bilinear programs of the form , maximized with respect to semidefinite constraints on and . Applied to the problem of quantum error correction this gives hierarchies of efficiently computable outer bounds on the optimal fidelity for any message dimension and error model. The first level of our hierarchies corresponds to the non-signalling assisted fidelity previously studied by [Leung & Matthews, IEEE Trans.~Inf.~Theory 2015], and positive partial transpose constraints can be added and used to give a sufficient criterion for the exact convergence at a given level of the hierarchy. To quantify the worst case convergence speed of our hierarchies, we derive novel quantum de Finetti theorems that allow imposing linear constraints on the approximating state. In…
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