English

Distributed multi-parameter quantum metrology with a superconducting quantum network

Quantum Physics 2026-02-27 v2

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 multi-parameter estimation remain limited, particularly due to the challenges of generating and distributing entanglement across a quantum network and dealing with incompatibilities in multi-parameter quantum metrology. Here we demonstrate distributed multi-parameter 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 multi-parameter distributed quantum metrology.

Keywords

Cite

@article{arxiv.2412.18398,
  title  = {Distributed multi-parameter quantum metrology with a superconducting quantum network},
  author = {Jiajian Zhang and Lingna Wang and Yong-Ju Hai and Jiawei Zhang and Ji Chu and Ji Jiang and Wenhui Huang and Yongqi Liang and Jiawei Qiu and Xuandong Sun and Ziyu Tao and Libo Zhang and Yuxuan Zhou and Yuanzhen Chen and Weijie Guo and Xiayu Linpeng and Song Liu and Wenhui Ren and Youpeng Zhong and Jingjing Niu and Haidong Yuan and Dapeng Yu},
  journal= {arXiv preprint arXiv:2412.18398},
  year   = {2026}
}

Comments

12+29 pages; 5+15 figures

R2 v1 2026-06-28T20:48:02.469Z