Second it allows us to prove a chain rule property for the sandwiched $alpha$-Rényi divergence for $alpha > 1$ which we use to characterize the strong converse exponent for channel discrimination. Maximum Likelihood (ML) quantum estimation problems are easily formed as log-convex optimization problems 1.
#QUANTUM ERROR CORRECTION VIA CONVEX OPTIMIZATION HOW TO#
I will present some ideas on how to do the tomography in this context. The constraint is the form required by the standard error-correction model upon which the optimization is constructed. 3 Quantum Error Correction, the Stabilizer Formalism, and Fault Toler-. How to do this, however, is an open question. First, we use it to get a converging hierarchy of upper bounds on the regularized sandwiched $alpha$-Rényi divergence between quantum channels for $alpha > 1$. There exist many families of algorithms, which, using non-classical logical. An important property of this new divergence is that its regularization is equal to the sandwiched (also known as the minimal) quantum Rényi divergence. size in simulations of quantum systems by using quantum systems themselves as simulators for other quantum systems. The advantage of this approach is that noisy channels which do not satisfy the standard assumptions for perfect correction can be optimized for the best possible encoding and/or recovery. Using fidelity measures leads naturally to a convex optimization problem, specifically a semidefinite program (SDP). This divergence has several desirable computational and operational properties such as an efficient semidefinite programming representation for states and channels, and a chain rule property. Recently and did this by posing error correction design as an optimization problem with the design variables being the process matrices associated with the encoding and/or recovery channels. We introduce a new quantum Rényi divergence $D^$ for $alpha in (1,infty)$ defined in terms of a convex optimization program. Abstract We show that the problem of designing a quantum information processing error correcting procedure can be cast as a bi-convex optimization problem.
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