Distributed multi-parameter quantum metrology with a superconducting quantum network

• URL: http://arxiv.org/abs/2412.18398v1
• Date: Tue, 24 Dec 2024 12:41:53 GMT
• Title: Distributed multi-parameter quantum metrology with a superconducting quantum network
• 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, Yuxuan Zhou, Yuanzhen Chen, Weijie Guo, Xiayu Linpeng, Song Liu, Wenhui Ren, Jingjing Niu, Youpeng Zhong, Haidong Yuan, Dapeng Yu,
• Abstract summary: We use a superconducting quantum network with low-loss interconnects to estimate multiple distributed parameters associated with non-commuting generators.<n>Our approach achieves an improvement of up to 6.86 dB over classical strategy for estimating all three components of a remote vector field in terms of standard deviation.
• Score: 12.826409453589461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum metrology has emerged as a powerful tool for timekeeping, field sensing, and precision measurements within fundamental physics. With the advent of distributed quantum metrology, its capabilities have been extended to probing spatially distributed parameters across networked quantum systems. However, generating the necessary non-local entanglement remains a significant challenge, and the inherent incompatibility in multi-parameter quantum estimation affects ultimate performance. Here we use a superconducting quantum network with low-loss interconnects to estimate multiple distributed parameters associated with non-commuting generators. By employing high-fidelity non-local entanglement across network nodes and a sequential control strategy, we accurately estimate remote vector fields and their gradients. Our approach achieves an improvement of up to 6.86 dB over classical strategy for estimating all three components of a remote vector field in terms of standard deviation. Moreover, for the estimation of gradients along two distinct directions across distributed vector fields, our distributed strategy, which utilizes non-local entanglement, outperforms local entanglement strategies, leading to a 3.44 dB reduction in the sum of variances.

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