Why initial system-environment correlations do not imply the failure of complete positivity: a causal perspective

TL;DR

This paper challenges the belief that initial system-environment correlations lead to non-completely positive evolution maps in quantum systems, proposing a causal framework that ensures complete positivity regardless of initial correlations.

Contribution

It identifies errors in the standard argument and introduces a causal perspective to define evolution maps that remain completely positive despite initial correlations.

Findings

Standard argument can produce non-linear or undefined maps.

A causal redefinition yields always completely positive maps.

Classical examples confirm the correctness of the causal approach.

Abstract

The common wisdom in the field of quantum information theory is that when a system is initially correlated with its environment, the map describing its evolution may fail to be completely positive. If true, this would have practical and foundational significance. We here demonstrate, however, that the common wisdom is mistaken. We trace the error to the standard argument for how the evolution map ought to be defined. We show that it sometimes fails to define a linear map or any map at all and that these pathologies persist even in completely classical examples. Drawing inspiration from the framework of classical causal models, we argue that the correct definition of the evolution map is obtained by considering a counterfactual scenario wherein the system is reprepared independently of any systems in its causal past while the rest of the circuit remains the same, yielding a map that is always completely positive. In a post-mortem on the standard argument, we highlight two distinct mistakes that retrospectively become evident (in its application to completely classical examples): (i) the types of constraints to which it appealed are constraints on what one can infer about the final state of a system based on its initial state, where such inferences are based not just on the cause-effect relation between them-which defines the correct evolution map-but also on the common cause of the two; (ii) in a (retrospectively unnecessary) attempt to introduce variability in the input state, it inadvertently introduced variability in the inference map itself, then tried to fit the input-output pairs associated to these different maps with a single map.