Distributed multi-parameter quantum metrology with a superconducting quantum network

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Authors: Jiajian Zhang, Lingna Wang, Yong-Ju Hai, Jiawei Zhang, Ji Chu, Ji Jiang, Wenhui Huang, Yongqi Liang, Jiawei Qiu, Xuandong Sun, Ziyu Tao, Libo Zhang
+10 more Yuxuan Zhou, Yuanzhen Chen, Weijie Guo, Xiayu Linpeng, Song Liu, Wenhui Ren, Youpeng Zhong, Jingjing Niu, Haidong Yuan, Dapeng Yu
Cited by: 4
References: 69

Abstract

Quantum metrology has emerged as a powerful tool for timekeeping, field sensing, and precision measurements in fundamental physics. With the advent of distributed quantum metrology, its capabilities have extended to probing spatially distributed parameters across networked quantum systems. However, scalable implementations of distributed quantum metrology with multiparameter estimation remain limited, particularly due to the challenges of generating and distributing entanglement across a quantum network and dealing with incompatibilities in multiparameter quantum metrology. Here we demonstrate distributed multiparameter quantum metrology on a modular superconducting quantum network with low-loss microwave interconnects, a platform that uniquely combines fast gate operations, adaptive control, and deterministic non-local entanglement generation. Using a control-enhanced sequential protocol, we estimate all three components of a remote vector field, achieving up to 13.72 dB improvement in precision over the individual strategy. We further perform direct estimation of vector field gradients along two directions across spatially separated nodes, realizing a 3.44 dB gain over local entanglement strategies. These results establish superconducting quantum networks as a competitive and reconfigurable platform for scalable multiparameter distributed quantum metrology.

Fields of Study & Subjects

Quantum Information and Cryptography Quantum Mechanics and Applications Advanced Thermodynamics and Statistical Mechanics

Publication details

Journal / source
Nature Communications
Volume
17
Issue
1
Pages
1825–1825
Publication date
20 January 2026
Language
EN