# Defining quantum divergences via convex optimization

arXiv: https://arxiv.org/abs/2007.12576 · HTML: https://mlsanity.com/paper/2007.12576/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Sum-of-Squares Relaxations for Information Theory and Variational Inference](https://mlsanity.com/paper/2206.13285/) (2022)

## Abstract (arXiv)

We introduce a new quantum R'enyi divergence D#α for α ∈ (1,∞) defined in terms of a convex optimization program. 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. An important property of this new divergence is that its regularization is equal to the sandwiched (also known as the minimal) quantum R'enyi divergence. This allows us to prove several results. First, we use it to get a converging hierarchy of upper bounds on the regularized sandwiched α-R'enyi divergence between quantum channels for α > 1. Second it allows us to prove a chain rule property for the sandwiched α-R'enyi divergence for α > 1 which we use to characterize the strong converse exponent for channel discrimination. Finally it allows us to get improved bounds on quantum channel capacities.