Journal of Physics B Atomic Molecular and Optical Physics

Parameter estimation via weak measurement with machine learning

Wenhuan Liu, Jingzheng Huang, Yanjia Li, Hongjing Li, Chen Fang, Yu Yang, Guihua Zeng

Published Jan 3, 2019• 7 citations

Abstract

Abstract Weak measurement technique can provide high sensitivity for parameter estimation tasks. However in practice, time-varying errors induced by noises significantly compromise its advantages. While prolonging the measurement time can reduce the effect of short-term correlated noises (e.g. white noise), it will on the other hand increase the estimation errors in the presence of long-term correlated noises(e.g. 1/ f and 1/ f 2 noises). The main obstacle of compensating these kind of errors is to precisely predict the time-varying trend of error, where the disturbances that causing error are highly unpredictable. In this work, we propose a weak measurement scheme with assistance of machine learning algorithm, which can ‘learn’ the time-varying trend of errors from data without an explicit model. In order to verify the feasibility of this scheme, we carry out a time-delay measurement experiment and achieve the noise compensation via machine learning algorithm. Experimental results demonstrate a 6 dB reduction of mean-square error comparing to the setup without machine learning, which suggest that our scheme can effectively improve the performance of weak measurement in the presence of long-time correlated noises.

DOI: 10.1088/1361-6455/aafbb0

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