Noise Classification in Three-Level Quantum Networks by Machine Learning

• URL: http://arxiv.org/abs/2405.01987v2
• Date: Mon, 02 Dec 2024 11:13:36 GMT
• Title: Noise Classification in Three-Level Quantum Networks by Machine Learning
• Abstract summary: We investigate a machine learning based classification of noise acting on a small quantum network.<n>We show that supervised learning can classify different types of classical dephasing noise affecting the system.<n>Our result paves the way for classifying spatial correlations of noise in quantum architectures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate a machine learning based classification of noise acting on a small quantum network with the aim of detecting spatial or multilevel correlations, and the interplay with Markovianity. We control a three-level system by inducing coherent population transfer exploiting different pulse amplitude combinations as inputs to train a feedforward neural network. We show that supervised learning can classify different types of classical dephasing noise affecting the system. Three non-Markovian (quasi-static correlated, anti-correlated and uncorrelated) and Markovian noises are classified with more than $99\%$ accuracy. On the contrary, correlations of Markovian noise cannot be discriminated with our method. Our approach is robust to statistical measurement errors and retains its effectiveness for physical measurements where only a limited number of samples is available making it very experimental-friendly. Our result paves the way for classifying spatial correlations of noise in quantum architectures.

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