Joint Optimization of Routing and Purification to Meet Fidelity Targets in Quantum Networks

TL;DR

This paper introduces a joint optimization scheme for routing and purification in quantum networks, using machine learning estimators to reduce latency and improve success rates while meeting fidelity targets.

Contribution

It presents a novel cost-based scheduler with hop-level estimators that adapt purification rounds, enhancing quantum network performance over fixed strategies.

Findings

Reduces mean latency by up to 8%.

Increases success rates by 14%.

Efficiently balances fidelity, latency, and resource consumption.

Abstract

Quantum networks rely on high-fidelity entanglement links, but achieving target fidelity often increases latency and Bell pair consumption due to purification. This paper proposes a cost-based scheduler that jointly optimizes path selection and purification round, along with two hop-level estimators (a Deep Neural Network classifier and a Bayesian optimizer) to predict the minimal purification rounds needed for target hop fidelity. The scheme flexibly adjusts final entanglement fidelity while minimizing latency, improving request success rates and efficient Bell pair usage. Simulations integrating purification, entanglement generation, and network-level scheduling show that our approach reduces mean latency by up to 8% and increases success rates by 14% compared to fixed-round purification with FIFO scheduling.