Joint Optimization of Routing and Purification to Meet Fidelity Targets in Quantum Networks
Year: 2026
Type: conference-paper
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.
Authors: Gongyu Ni, Holger Claussen, Lester Ho
Institution: Tyndall National Institute
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4417301064
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