[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120679-en":3,"doc-seo-120679-105":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":20,"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},120679,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","A didactic approach to quantum machine learning with a single qubit","A didactic framework for quantum machine learning (QML) is presented through an explicit, hands-on example using a real-world dataset. The focus is learning with a single qubit via data re-uploading techniques, supported by an overview of relevant background in quantum computing and machine learning. Data re-uploading models are thoroughly explained and implemented in both toy and real-world settings using the qiskit SDK. Results indicate layer count strongly affects final accuracy, while single-qubit classifiers can reach performance comparable to classical baselines under the same training conditions, motivating further research questions.","arXiv :2211 . 13191v2 [ quant-ph] 8 Apr 2023  \nA didactic approach to quantum machine learning with a single qubit  \nElena Pe~na Tapia 1 , Giannicola Scarpa2 , and Alejandro Pozas-Kerstjens3  \n1 Universidad Polit􀀓ecnica de Madrid, Madrid, 28031, Spain  \n2 Escuela T􀀓enica Superior de Ingenier􀀓􀀐a de Sistemas Inform􀀓aticos, Universidad Polit􀀓ecnica de Madrid, Madrid, 28031, Spain  \n3 Instituto de Ciencias Matem􀀓aticas (CSIC-UAM-UC3M-UCM), 28049 Madrid, Spain  \nAbstract. This paper presents, via an explicit example with a real-world dataset, a hands-on introduction to the 􀀌eld of quantum machine learning (QML) . We focus on the case of learning with a single qubit, using data re-uploading techniques. After a discussion of the relevant background in quantum computing and machine learning we provide a thorough explanation of the data re-uploading models that we consider, and implement the di􀀋erent proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK. We 􀀌nd that, as in the case of classical neural networks, the number of layers is a determining factor in the 􀀌nal accuracy of the models. Moreover, and interestingly, the results show that single-qubit classi􀀌ers can achieve a performance that is on-par with classical counterparts under the same set of training conditions. While this cannot be understood as a proof of the advantage of quantum machine learning, it points to a promising research direction, and raises a series of questions that we outline.  \nA didactic approach to quantum machine learning with a single qubit 2  \n1. Introduction  \nQuantum Machine Learning (QML) has become one of the hottest trends in the area of quantum computing and quantum information science of 2022 . However, QML is also a 􀀌eld in the making, so much so that it is di􀀎cult to 􀀌nd a consensual \\o􀀎cial\" de􀀌nition of the term. In its most general sense, quantum machine learning explores synergies between quantum computing and machine learning [1, 2] . This broad de􀀌nition for QML can apply to a wide variety of approaches, from the use of machine learning in experimental physics [3{6] to quantum-inspired machine learning [7{10], includingthe approach we focus on for this paper: the development of quantum algorithms for machine learning tasks, with the intention of potentially \\learning better\". This leads to another point to consider, and that is what does \\learning better\" mean, and the answer is not easy either. The concept of \\quantum advantage\" (how can a quantum computer outperform its classical counterpart) is still an open question itself, and while some works have proven certain advantages in speci􀀌c learning problems, these are still quite narrow, and heavy considerations are taken into account.  \nDespite these pending questions, there are reasons to believe that combining quantum computing and machine learning can still be a very interesting path to pursue, both from the theoretical and practical point of view. As mentioned in Ref. [11], machine learning models are not theoretically well understood, their advantage is not necessarily mathematically proven, and still they are able to perform amazingly well in a wide variety of tasks. At the same time, the authors of Ref. [2] point out how the 􀀌eld of machine learning is su􀀋ering from a lack of fundamentally new research directions. By studying its intersection with quantum computing, new insights could arise to improve our current understanding of these models and open new ways for improving their performance and e􀀎ciency.  \nThe motivation behind this paper arises from the study of QML models from the point of view of a potential \\customer\", trying to look for the most appropriate model for a speci􀀌c commercial application. Most current e􀀋orts in QML are centered in algorithms that can be implemented in currently available quantum devices and still provide some relevant learning capability. In this context of NISQ-focused implementations [12], the proposal of ","cbCaisNwfwStmgNK","https://ap.wps.com/l/cbCaisNwfwStmgNK","pdf",12280176,1,25,"English","en",105,"# Introduction\n## Motivation and open questions\n## Single-qubit learning in practical tasks\n## Paper organization","[{\"question\":\"What quantum machine learning approach does the paper teach and implement?\",\"answer\":\"It teaches learning with a single qubit using data re-uploading techniques, explaining the considered models and implementing them with qiskit.\"},{\"question\":\"What dataset is used to test the single-qubit models in a real-world setting?\",\"answer\":\"The paper trains and evaluates a single-qubit QNN model on the Kaggle credit card fraud detection dataset.\"},{\"question\":\"How does the paper evaluate performance compared with classical methods?\",\"answer\":\"It benchmarks the single-qubit quantum model against a classical neural network algorithm under the same training conditions, finding comparable performance despite no formal quantum advantage proof.\"}]","A didactic approach to quantum machine learning with a single qubit | 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quantum machine learning approach does the paper teach and implement?","Question",{"text":75,"@type":76},"It teaches learning with a single qubit using data re-uploading techniques, explaining the considered models and implementing them with qiskit.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset is used to test the single-qubit models in a real-world setting?",{"text":80,"@type":76},"The paper trains and evaluates a single-qubit QNN model on the Kaggle credit card fraud detection dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper evaluate performance compared with classical methods?",{"text":84,"@type":76},"It benchmarks the single-qubit quantum model against a classical neural network algorithm under the same training conditions, finding comparable performance despite no formal quantum advantage 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