Superiority of Krylov shadow tomography in estimating quantum Fisher information: From bounds to exactness

TL;DR

This paper demonstrates that Krylov shadow tomography provides efficient, accurate, and sometimes exact estimation of quantum Fisher information, outperforming previous polynomial bounds, with rapid convergence and practical relevance.

Contribution

It reveals that low-order Krylov bounds can efficiently estimate or exactly match the QFI, surpassing prior polynomial bounds and enhancing practical quantum information applications.

Findings

Krylov bounds converge exponentially fast to the QFI with increasing order.

Low-order Krylov bounds can exactly match the QFI for low-rank states.

The approach outperforms existing polynomial lower bounds in efficiency and accuracy.

Abstract

Estimating the quantum Fisher information (QFI) is a crucial yet challenging task with widespread applications across quantum science and technologies. The recently proposed Krylov shadow tomography (KST) opens a new avenue for this task by introducing a series of Krylov bounds on the QFI. In this work, we address the practical applicability of the KST, unveiling that the Krylov bounds of low orders already enable efficient and accurate estimation of the QFI. We show that the Krylov bounds converge to the QFI exponentially fast with increasing order and can surpass the state-of-the-art polynomial lower bounds known to date. Moreover, we show that certain low-order Krylov bound can already match the QFI exactly for low-rank states prevalent in practical settings. Such exact match is beyond the reach of polynomial lower bounds proposed previously. These theoretical findings, solidified by extensive numerical simulations, demonstrate practical advantages over existing polynomial approaches, holding promise for fully unlocking the effectiveness of QFI-based applications.