Machine learning classification of non-Markovian noise disturbing quantum dynamics

Year: 2023

Authors: Martina S.; Gherardini S.; Caruso F.

Autors Affiliation: Univ Florence, Dept of Phys & Astron, via G Sansone 1, I-50019 Sesto Fiorentino, Italy; Univ Florence, European Lab Nonlinear Spect LENS, Via Nello Carrara 1, I-50019 Sesto Fiorentino, Italy; CNR INO, Area Sci Pk, I-34149 Trieste, Italy.

Abstract: In this paper machine learning and artificial neural network models are proposed for the classification of external noise sources affecting a given quantum dynamics. For this purpose, we train and then validate support vector machine, multi-layer perceptron and recurrent neural network models with different complexity and accuracy, to solve supervised binary classification problems. As a result, we demonstrate the high efficacy of such tools in classifying noisy quantum dynamics using simulated data sets from different realizations of the quantum system dynamics. In addition, we show that for a successful classification one just needs to measure, in a sequence of discrete time instants, the probabilities that the analysed quantum system is in one of the allowed positions or energy configurations. Albeit the training of machine learning models is here performed on synthetic data, our approach is expected to find application in experimental schemes, as e.g. for the noise benchmarking of noisy intermediate-scale quantum devices.

Journal/Review: PHYSICA SCRIPTA

Volume: 98 (3)      Pages from: 035104-1  to: 035104-18

More Information: The authors were financially supported from by the Fondazione Cassa di Risparmio di Firenze through the project QUANTUM-AI, the European Union´s Horizon 2020 research and innovation programme under FET-OPEN Grant Agreement No. 828946 (PATHOS), and from University of Florence through the project Q-CODYCES.
KeyWords: Machine learning classification, Non-Markovian noise, Recurrent neural networks
DOI: 10.1088/1402-4896/acb39b

ImpactFactor: 2.600
Citations: 5
data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-11-24
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