Non-Markovianity-based ultrasensitive parameter estimation

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Authors: Olli Siltanen
Cited by: 0
References: 63

Abstract

Accurate parameter estimation is a central task in quantum metrology and sensing, where quantum resources can provide precision beyond classical limits. In realistic settings, however, system-environment interactions lead to decoherence, reducing these strategies to their classical counterparts. Noise is typically classified as Markovian or non-Markovian, with the latter often preserving quantum coherence longer and thus supporting better metrological performance. Still, the absence of noise is generally considered ideal. In this work, we uncover a striking reversal: certain non-Markovian environments not only outperform Markovian ones - including their quantum Cramér-Rao bounds - but can also surpass the entirely noiseless case. We demonstrate these findings numerically for an all-optical setup, which is experimentally feasible and can be extended to other physical platforms. In general, our results open new avenues for noise-assisted quantum metrology beyond conventional limits.

Fields of Study & Subjects

Neural Networks and Reservoir Computing Quantum Information and Cryptography Quantum Computing Algorithms and Architecture

Publication details

Journal / source
Physical Review A
Publisher
American Physical Society (APS)
Volume
113
Issue
6
Publication date
26 May 2026
Language
EN