Efficient approximation of regularized relative entropies and applications

TL;DR

This paper develops efficient methods to approximate regularized quantum relative entropies, enabling practical computation for quantum information tasks and resource theories, with applications to channel discrimination and entanglement measures.

Contribution

It introduces a polynomial-size quantum relative entropy program for approximating regularized relative entropies under structural assumptions, enhancing computational efficiency.

Findings

Efficient approximation of regularized quantum relative entropy within additive error.

Application to adversarial quantum channel discrimination.

Improved bounds for entanglement cost and distillation processes.

Abstract

The quantum relative entropy is a fundamental quantity in quantum information science, characterizing the distinguishability between two quantum states. However, this quantity is not additive in general for correlated quantum states, necessitating regularization for precise characterization of the operational tasks of interest. Recently, we proposed the study of the regularized relative entropy between two sequences of sets of quantum states in [arXiv:2411.04035], which captures a general framework for a wide range of quantum information tasks. Here, we show that given suitable structural assumptions and efficient descriptions of the sets, the regularized relative entropy can be efficiently approximated within an additive error by a quantum relative entropy program of polynomial size. This applies in particular to the regularized relative entropy in adversarial quantum channel discrimination. Moreover, we apply the idea of efficient approximation to quantum resource theories. In particular, when the set of interest does not directly satisfy the required structural assumptions, it can be relaxed to one that does. This provides improved and efficient bounds for the entanglement cost of quantum states and channels, entanglement distillation and magic state distillation. Numerical results demonstrate improvements even for the first level of approximation.