.png){kind=icon}
.png){kind=icon}
Return
BTS-QSN: Budgeting and Task Scheduling in Quantum Sensor Networks
.png){kind=icon}
PRE
AI
H
H
DOI：10.1109/jsen.2026.3707123.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Quantum sensing can surpass classical precision limits, and interconnecting spatially separated sensors via quantum links enables distributed quantum sensing networks (DQSNs) with enhanced collective estimation capabilities through entanglement and squeezing. In practical deployments, however, entanglement is scarce, imperfect, and vulnerable to noise. It may require purification, degrade during storage and routing, and be consumed by sensing tasks. These constraints make the scheduling of concurrent sensing jobs challenging, particularly when tasks have heterogeneous fidelity, utility, priority, and deadline requirements. To address this challenge, we propose BTS-QSN, a reinforcement-learning-based framework for joint entanglement budgeting and task scheduling in DQSNs. BTS-QSN treats entanglement as a consumable, lossy resource whose generation, purification, storage, and allocation must be coordinated over time. We evaluate the framework across multiple network scenarios, performance metrics, baseline strategies, and a representative application-specific scenario. The results show that BTS-QSN effectively manages quantum resources under diverse operating conditions while improving sensing utility and task timeliness.
Keywords:
Distributed quantum sensing networks (DQSNs)
entanglement resource management
noise-aware scheduling
quantum communication
quantum magnetometry
quantum sensing
reinforcement learning (RL)
task scheduling
Journal
IF:
4.5
Papers:
2.1W
Citations:
7.3W
Organization
Cited Papers