Entanglement Distribution in the Quantum Internet: Knowing when to Stop!

TL;DR

This paper develops a theoretical framework to determine the optimal stopping point for entanglement distribution in the Quantum Internet, considering quantum noise and decoherence effects, to improve efficiency and reliability.

Contribution

It models entanglement distribution as a Markov decision process and derives optimal stopping policies to enhance quantum network performance.

Findings

Entanglement distribution can be modeled as a Markov decision process.

Optimal policies have attractive features reducing computational complexity.

Framework aids in designing more reliable quantum entanglement distribution protocols.

Abstract

Entanglement distribution is a key functionality of the Quantum Internet. However, quantum entanglement is very fragile, easily degraded by decoherence, which strictly constraints the time horizon within the distribution has to be completed. This, coupled with the quantum noise irremediably impinging on the channels utilized for entanglement distribution, may imply the need to attempt the distribution process multiple times before the targeted network nodes successfully share the desired entangled state. And there is no guarantee that this is accomplished within the time horizon dictated by the coherence times. As a consequence, in noisy scenarios requiring multiple distribution attempts, it may be convenient to stop the distribution process early. In this paper, we take steps in the direction of knowing when to stop the entanglement distribution by developing a theoretical framework, able to capture the quantum noise effects. Specifically, we first prove that the entanglement distribution process can be modeled as a Markov decision process. Then, we prove that the optimal decision policy exhibits attractive features, which we exploit to reduce the computational complexity. The developed framework provides quantum network designers with flexible tools to optimally engineer the design parameters of the entanglement distribution process.