Yang Xiaodong, Chen Xi, Li Jun, Peng Xinhua, Laflamme Raymond
Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei, 230026, China.
Institute for Quantum Computing and Department of Physics and Astronomy, University of Waterloo, Waterloo, N2L 3G1, ON, Canada.
Sci Rep. 2021 Jan 12;11(1):672. doi: 10.1038/s41598-020-80070-1.
Quantum metrology plays a fundamental role in many scientific areas. However, the complexity of engineering entangled probes and the external noise raise technological barriers for realizing the expected precision of the to-be-estimated parameter with given resources. Here, we address this problem by introducing adjustable controls into the encoding process and then utilizing a hybrid quantum-classical approach to automatically optimize the controls online. Our scheme does not require any complex or intractable off-line design, and it can inherently correct certain unitary errors during the learning procedure. We also report the first experimental demonstration of this promising scheme for the task of finding optimal probes for frequency estimation on a nuclear magnetic resonance (NMR) processor. The proposed scheme paves the way to experimentally auto-search optimal protocol for improving the metrology precision.
量子计量学在许多科学领域都发挥着基础性作用。然而，工程化纠缠探针的复杂性以及外部噪声为在给定资源下实现待估计参数的预期精度带来了技术障碍。在此，我们通过在编码过程中引入可调控制，然后利用混合量子 - 经典方法在线自动优化控制来解决这一问题。我们的方案不需要任何复杂或棘手的离线设计，并且在学习过程中能够固有地纠正某些酉误差。我们还报告了该有前景方案在核磁共振（NMR）处理器上用于寻找频率估计最优探针任务的首次实验演示。所提出的方案为通过实验自动搜索最优协议以提高计量精度铺平了道路。
