PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 21, 2026Optics Express0 citationsOpen Access

Quantum metrology under coarse-grained measurement

View Full Paper
BGByeong-Yoon GoGGGeunhee GwakYYYoung-Do Yoon

Key Points

Key points are not available for this paper at this time.

Abstract

While quantum metrology enables measurement precision beyond classical limits, its performance is often susceptible to experimental imperfections. Most prior studies have focused on imperfections in quantum states and operations. Here, we investigate the effect of coarse graining in quantum measurement through both theoretical analysis and experimental demonstration. Using an interferometer with a squeezed vacuum and a laser input, we analyze how coarse graining in homodyne detection affects the precision of phase estimation. We evaluate the Fisher information under various coarse-graining conditions and determine, in each case, an optimal estimation strategy that saturates the Cramér-Rao bound. Remarkably, even extremely coarse-grained measurement—with only two bins—enables phase estimation beyond the standard quantum limit and even achieves a precision that follows the Heisenberg scaling. We experimentally demonstrate quantum-enhanced phase estimation under coarse-grained homodyne detection. To determine an optimal estimation strategy, we employ the method of moments and present calibration procedures that enable its application to general experimental settings. Using only two bins, we observe a quantum enhancement of 1.2 dB compared to the classical method using the ideal measurement, improving towards 3.0 dB as the bin number increases. These results highlight a practical pathway to achieving quantum enhancement in the presence of severe experimental imperfections.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Go et al. (2026) studied this question.

synapsesocial.com/papers/6a18100e7c70e6dd4312a38fhttps://doi.org/10.1364/oe.592671
Ask AI
Helpful
Bookmark
Share
View Full Paper