The Effect of Non-Gaussian Noise on Auto-correlative Weak-value Amplification

• URL: http://arxiv.org/abs/2209.12732v2
• Date: Tue, 27 Sep 2022 00:58:51 GMT
• Title: The Effect of Non-Gaussian Noise on Auto-correlative Weak-value Amplification
• Authors: Jing-Hui Huang, J. S. Lundeen, Adetunmise C. Dada, Kyle M.Jordan, Guang-Jun Wang, Xue-Ying Duan and Xiang-Yun Hu
• Abstract summary: We study the effect of non-Gaussian noise on the auto-correlative weak-value amplification (AWVA) technique. In particular, two types of noise with a negative-dB signal-to-noise ratio, frequency-stationary noises and frequency-nonstationary noises are studied.
• Score: 3.7637002490536804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate knowledge of the spectral features of noise and their influence on open quantum systems is fundamental for quantitative understanding and prediction of the dynamics in a realistic environment. For the weak measurements of two-level systems, the weak value obtained from experiments will inevitably be affected by the noise of the environment. Following our earlier work on the technique of the auto-correlative weak-value amplification (AWVA) approach under a Gaussian noise environment, here we study the effect of non-Gaussian noise on the AWVA technique.In particular, two types of noise with a negative-dB signal-to-noise ratio, frequency-stationary noises and frequency-nonstationary noises are studied. The various frequency-stationary noises, including low-frequency (1/f) noises, medium-frequency noises, and high-frequency noises, are generated in Simulink by translating the Gaussian white noise with different band-pass filters. While impulsive noise is studied as an example of frequency-non stationary noises. Our simulated results demonstrate that 1/f noises and impulsive noises have greater disturbance on the AWVA measurements. In addition, adding one kind of frequency-stationary noise, clamping the detected signals, and dominating the measurement range may {have} the potential to improve the precision of the AWVA technique with both a smaller deviation of the mean value and a smaller error bar in the presence of many hostile non-Gaussian noises.

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