[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119677-en":3,"doc-seo-119677-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119677,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Machine learning approach for quantum non-Markovian noise classification","Machine learning and artificial neural network models are developed for supervised quantum noise classification in stochastic quantum dynamics, targeting binary decisions between distinct noise behaviors. Support vector machines, multi-layer perceptrons, and recurrent neural networks with different complexity are trained and validated using data drawn from single realisations of quantum system evolution. Using the quantum random walk formalism, the approach identifies non-Markovian dynamics via time-correlation features without requiring measurements of quantum coherences or externally driven control-pulse sequences. With a priori training on synthetic data, the method supports experimental noise benchmarking and quantum sensing based on discrete-time position or energy probabilities.","View metadata, citation and similar [papers at ](papers at core.ac.uk)[core.ac.uk](papers at core.ac.uk) brought to you by CORE  \n[provided by](provided by arXiv.org)[ arXiv.org](provided by arXiv.org) e-Print Archive  \narXiv :2101 .03221v1 [ quant-ph] 8 Jan 2021  \nMachine learning approach for quantum non-Markovian noise classification  \nStefano Martina,∗ Stefano Gherardini,† and Filippo Caruso‡ Dept. of Physics and Astronomy & European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, via G. Sansone 1, 50019 Sesto Fiorentino, Italy.  \nIn this paper, machine learning and artificial neural network models are proposed for quantum noise classification in stochastic 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. By exploiting the quantum random walk formalism, we demonstrate the high efficacy of such tools in classifying noisy quantum dynamics using data sets collected in a single realisation of the quantum system evolution. In addition, we also 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, without any external driving. Thus, neither measurements of quantum coherences nor sequences of control pulses are required. Since in principle the training of the machine learning models can be performed a-priori on synthetic data, our approach is expected to find direct application in a vast number of experimental schemes and also for the noise benchmarking of the already available noisy intermediate-scale quantum devices.  \nNoise sensing aims at discriminating, and possibly reconstructing, noise profiles that affect static parameters and dynamical variables of the evolution of classical and quantum systems [1–4] . Regarding the latter, which constitute the main object of our discussion, noise usually destroys partially the coherent evolution of the investigated (open) quantum system, interacting with an external environment or simpler with other quantum or classical systems [5, 6] . In such scenario, noise can be generally modelled as a stochastic process, distributed according to an unknown probability distribution [7, 8] . Several techniques, at both the theoretical and experimental side, have been recently developed for the inference of the unknown noise distribution and detect, if present, non-zero time-correlations among adjacent samples (over time) of the noise process [9–18] . However, most of them suffer of the need to be able to fully, or almost totally, control the quantum system, so as to generate multiple control sequences (e.g., dynamical decoupling ones [19–21]) each of them being sensitive to a different component of the noise to be inferred [22, 23] . Moreover, in reference [24] a diagnostic protocol for the detection of correlations among arbitrary sets of qubits have been lately tested on a 14-qubit superconducting quantum architecture, by discovering the persistent presence of long-range two-qubit correlations.  \nIn this paper, we exploit for the first time powerful statistical tool, based on Machine Learning (ML) techniques [25, 26], to efficiently carry out quantum noise classification with high accuracy. The proposed methods are designed to distinguish between Independent and Identically Distributed (i.i.d.) noise sequences and noise samples originated by a non-trivial memory kernel, thus characterised by specific time-correlation parameters. It  \n∗ stefano.martina@unifi .it † gherardini@lens.unifi .it ‡ filippo.caruso@unifi .it  \nis worth reminding that, in the latter case, the dynamics of the stochastic quantum system turns out of being non-Markovian, in the sense that samples of its state in different time instants are correlated [27, 28] . This entails that","cbCaigdXI1Jo1mAx","https://ap.wps.com/l/cbCaigdXI1Jo1mAx","pdf",723965,1,14,"English","en",105,"# Introduction\n## Noise sensing and limitations of existing protocols\n# Proposed approach\n## Machine learning for quantum non-Markovian noise classification\n## Supervised binary classification between noise types\n# Model training and data generation\n## Support Vector Machines, MLPs, and RNNs\n## Dataset setup and achievable accuracy\n# Advantages and implications","[{\"question\":\"What does the paper aim to achieve in quantum noise classification?\",\"answer\":\"It proposes machine learning models to classify quantum noise in stochastic quantum dynamics using supervised binary classification, distinguishing different noise behaviors including non-Markovian effects.\"},{\"question\":\"How does the method detect non-Markovian noise without advanced control?\",\"answer\":\"By using probabilities of the quantum system’s positions or energy configurations at discrete time instants, the approach infers time-correlations without requiring measurements of quantum coherences or sequences of control pulses.\"},{\"question\":\"Which machine learning models are trained and what performance is reported?\",\"answer\":\"The paper trains support vector machines, multi-layer perceptrons, and recurrent neural networks on generated datasets, reporting classification accuracy up to 97%.\"}]","Machine learning approach for quantum non-Markovian noise classification | 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does the paper aim to achieve in quantum noise classification?","Question",{"text":75,"@type":76},"It proposes machine learning models to classify quantum noise in stochastic quantum dynamics using supervised binary classification, distinguishing different noise behaviors including non-Markovian effects.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method detect non-Markovian noise without advanced control?",{"text":80,"@type":76},"By using probabilities of the quantum system’s positions or energy configurations at discrete time instants, the approach infers time-correlations without requiring measurements of quantum coherences or sequences of control pulses.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are trained and what performance is reported?",{"text":84,"@type":76},"The paper trains support vector machines, multi-layer perceptrons, and recurrent neural networks on generated datasets, reporting classification accuracy up to 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