Entangled sensor-networks for dark-matter searches
Anthony J. Brady, Christina Gao, Roni Harnik, Zhen Liu, Zheshen Zhang, and Quntao Zhuang

TL;DR
This paper develops a theoretical framework for using entangled sensor networks with quantum squeezing to enhance dark matter axion searches, combining signals coherently and reducing noise via multipartite entanglement.
Contribution
It introduces a novel approach to leverage quantum entanglement and squeezing across multiple sensors for improved axion detection in dark matter research.
Findings
Entangled sensor networks can coherently combine signals for better sensitivity.
Multipartite entanglement reduces noise across the sensor network.
GKP states offer limited practical advantage over squeezed states in this context.
Abstract
The hypothetical axion particle (of unknown mass) is a leading candidate for dark matter (DM). Many experiments search for axions with microwave cavities, where an axion may convert into a cavity photon, leading to a feeble excess in the output power of the cavity. Recent work [Nature 590, 238 (2021)] has demonstrated that injecting squeezed vacuum into the cavity can substantially accelerate the axion search. Here, we go beyond and provide a theoretical framework to leverage the benefits of quantum squeezing in a network setting consisting of many sensor-cavities. By forming a local sensor network, the signals among the cavities can be combined coherently to boost the axion search. Furthermore, injecting multipartite entanglement across the cavities -- generated by splitting a squeezed vacuum -- enables a global noise reduction. We explore the performance advantage of such a local,…
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