Process tensor distinguishability measures

TL;DR

This paper compares two types of measures for distinguishing quantum processes, showing that generalized divergences satisfy important data processing inequalities and are more suitable for resource theories involving quantum combs.

Contribution

The paper analyzes and compares Choi divergences and generalized divergences for quantum combs, establishing that generalized divergences obey data processing inequalities and are more appropriate for quantum process discrimination.

Findings

Choi divergences do not satisfy data processing inequalities.

Generalized divergences satisfy data processing inequalities.

Generalized divergences are more suitable for resource theories involving quantum combs.

Abstract

Process tensors are quantum combs describing the evolution of open quantum systems through multiple steps of a quantum dynamics. While there is more than one way to measure how different two processes are, special care must be taken to ensure quantifiers obey physically desirable conditions such as data processing inequalities. Here, we analyze two classes of distinguishability measures commonly used in general applications of quantum combs. We show that the first class, called Choi divergences, does not satisfy an important data processing inequality, while the second one, which we call generalized divergences, does. We also extend to quantum combs some other relevant results of generalized divergences of quantum channels. Finally, given the properties we proved, we argue that generalized divergences may be more adequate than Choi divergences for distinguishing quantum combs in most of their applications. Particularly, this is crucial for defining monotones for resource theories whose states have a comb structure, such as resource theories of quantum processes and resource theories of quantum strategies.