Jaynes principle versus entanglement

TL;DR

The paper compares Jaynes' maximum entropy inference with an entanglement minimization approach, revealing that the former can incorrectly suggest entanglement in compatible states, affecting entanglement processing.

Contribution

It demonstrates through examples that Jaynes' scheme may produce false positives for entanglement, proposing entanglement minimization as a more reliable inference method.

Findings

Jaynes' inference can produce inseparable states with compatible data.

Minimizing entanglement avoids false positives in state inference.

The difficulty with Jaynes' scheme disappears when using entanglement minimization.

Abstract

We show, by explicit examples, that the Jaynes inference scheme based on maximization of entropy can produce inseparable states even if there exists a separable state compatible with the measured data. It can lead to problems with processing of entanglement. The difficulty vanishes when one uses inference scheme based on minimization of entanglement.