Xiaodie Lin, Haidong Yuan

Quantum metrology stands as a leading application of quantum science and technology, yet its precision and sensitivity are often constrained by noise. In the context of near-term quantum metrology, quantum error mitigation offers a promising strategy to leverage quantum resources. While existing error-mitigated protocols largely depend on virtual state purification, significant noise accumulation and the additional noise introduced by the noisy implementations of these protocols can impede the effectiveness. To address these problems, we propose probabilistic virtual channel purification to handle the largely accumulated noise while efficiently canceling additional noise from itself. This also naturally leads to an enhanced version of virtual state purification, namely probabilistic virtual state purification. Within the sequential scheme of quantum metrology, our error analysis reveals a significant reduction in bias and a quantum advantage in sampling cost when the number of channels encoding the interested parameters is $O(p^{-1})$, where $p$ is the error rate of the encoding channel. In this range, both probabilistic virtual purification methods demonstrate significant improvements in parameter estimation precision and robustness against practical noise, as evidenced by numerical simulations for both single- and multi-parameter tasks.