Non-Markovian Models of Environmentally-driven Disentanglement in Molecular Charge Qubits

TL;DR

This paper develops non-Markovian models to describe how environmental interactions cause disentanglement in molecular charge qubits, revealing Gaussian decay behaviors that Markovian models cannot capture.

Contribution

It introduces a new approximate non-Markovian model that captures Gaussian decay of entanglement in molecular charge qubits due to environmental interactions.

Findings

Gaussian decay of coherences and entanglement is characterized.

Time scale of disentanglement relates to interaction strength.

Model generalizes to environments with randomness.

Abstract

Models of quantum disentanglement are developed for nanometer-scale molecular charge qubits (MCQs). Two MCQs, $A$ and $B$, are prepared in a Bell state and separated for negligible $A$-$B$ interactions. Interactions between the local environment and each MCQ unravels $A$-$B$ entanglement during coherent system+environment evolution. Three models are used for dynamics: (1) a previously-developed, numerical model, in which both $AB$ and environment $\mathcal{E}$ are modeled explicitly; (2) an exact, semi-analytic model, in which only the dynamics of $AB$ are calculated, and (3) an approximate model developed from the semi-analytic model and assumptions about randomness in $\mathcal{E}$. In the approximate model, the non-zero coherences of the density operator for $AB$ decay with a Gaussian time dependence. This provides a time scale for system dynamics in the exact models as well. This time scale is related directly to the strength of AB-$\mathcal{E}$ interaction. This time scale describes cases where environmental interaction with one target MCQ is dominant, generalizing a previous time scale applicable only when both MCQs have roughly the same strength of interaction with the local environment. Entanglement is measured using two-qubit correlation functions, the dynamics of which are used to demonstrate the effectiveness of the time scale. The early-time decay of coherences and the loss of entanglement is well-characterized as Gaussian, a behavior that Markovian models for memoryless environments cannot capture. The approximate Gaussian model may be used to describe the dynamics of MCQ disentanglement under the influence of environments modeled here, as well as other environments where randomness is present.