Entanglement Allocation for Quantum Integrated Sensing and Communication
Abstract
Jointly supporting communication and distributed sensing over a shared, limited pool of entanglement resources is an attractive prospect for future quantum networks. We develop a network-level quantum integrated sensing and communication (Q-ISAC) framework in which a quantum base station dynamically allocates elementary Bell pairs between communication users and distributed sensing. Sensing resources are adaptively partitioned among multiple GHZ probes with dynamically selected sizes and sensing-node assignments. We derive an effective quantum Fisher information model accounting for probabilistic fusion, transmission loss, and memory decoherence, and formulate a finite-horizon allocation problem subject to communication QoS, expected sensing-information, sensing-reliability, and hardware constraints. A hierarchical constrained proximal policy optimization (HC-PPO) framework combines learned discrete sensing allocation with analytical continuous communication allocation. Numerical results show that GHZ probing requires approximately $27$--30% fewer sensing qubits than separable probing under modest probe-size caps and 65-67% fewer at the highest considered target under the same sensing requirements. Adaptive multi-GHZ partitioning provides greater robustness than single-GHZ sensing, while adaptive communication--sensing sharing outperforms the evaluated fixed splits. The proposed HC-PPO framework satisfies the joint requirements in at least 99.9% of test instances across the considered sensing targets and fusion probabilities. Monte Carlo evaluation separately shows that realized terminal information reached its target in 96-98% of realizations, distinguishing expected-information satisfaction from realized sensing performance.
Related
- Gaussian Quantum Integrated Sensing and Communication
- The Sample Complexity of Quantum Entanglement Allocation
- Classical communication over quantum channels: does entanglement help in nature?
- An exponential separation between entanglement-assisted and unassisted one-way quantum communication
- Gaussian entanglement and quantum communication: geodesic optimization and multimode bounds
BibTeX
@misc{taskooh2026entanglement,
title = {{Entanglement Allocation for Quantum Integrated Sensing and Communication}},
author = {Shiva Kazemi Taskooh and Mehdi Monemi and Ekram Hossain and Georges Kaddoum and Mehdi Rasti},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.04128},
eprint = {2611.04128},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.04128},
}
RIS
TY - UNPB
TI - Entanglement Allocation for Quantum Integrated Sensing and Communication
AU - Shiva Kazemi Taskooh
AU - Mehdi Monemi
AU - Ekram Hossain
AU - Georges Kaddoum
AU - Mehdi Rasti
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.04128
UR - https://arxiv.org/abs/2611.04128
AB - Jointly supporting communication and distributed sensing over a shared, limited pool of entanglement resources is an attractive prospect for future quantum networks. We develop a network-level quantum integrated sensing and communication (Q-ISAC) framework in which a quantum base station dynamically allocates elementary Bell pairs between communication users and distributed sensing. Sensing resources are adaptively partitioned among multiple GHZ probes with dynamically selected sizes and sensing-node assignments. We derive an effective quantum Fisher information model accounting for probabilistic fusion, transmission loss, and memory decoherence, and formulate a finite-horizon allocation problem subject to communication QoS, expected sensing-information, sensing-reliability, and hardware constraints. A hierarchical constrained proximal policy optimization (HC-PPO) framework combines learned discrete sensing allocation with analytical continuous communication allocation. Numerical results show that GHZ probing requires approximately $27$--30% fewer sensing qubits than separable probing under modest probe-size caps and 65-67% fewer at the highest considered target under the same sensing requirements. Adaptive multi-GHZ partitioning provides greater robustness than single-GHZ sensing, while adaptive communication--sensing sharing outperforms the evaluated fixed splits. The proposed HC-PPO framework satisfies the joint requirements in at least 99.9% of test instances across the considered sensing targets and fusion probabilities. Monte Carlo evaluation separately shows that realized terminal information reached its target in 96-98% of realizations, distinguishing expected-information satisfaction from realized sensing performance.
ER -