[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118057-en":3,"doc-seo-118057-105":30,"detail-sidebar-cat-0-en-105":92},{"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},118057,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Systematic literature review - Quantum machine learning and its applications","Quantum physics has reshaped scientific understanding, and quantum computing enables calculations through quantum-mechanical phenomena such as entanglement and superposition. Despite theoretical promise, current quantum devices lack sufficient qubits and fault tolerance, limiting broad advantages. In response, this systematic literature review summarizes and classifies quantum machine learning algorithms and their applications published from 2017 to 2023, identifying 94 relevant articles and examining circuit or ansatz implementations, including quantum versions of classical methods and quantum neural networks.","| Review article\u003Cbr>Systematic literature review: Quantum machine learning and its applications David Peral-Garcíaa,∗, Juan Cruz-Benito b, Francisco José García-Peñalvo c\u003Cbr>a Expert Systems and Applications Laboratory - ESALAB, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain b IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA\u003Cbr>c GRIAL Research Group, Department of Computers and Automatics, Research Institute for Educational Sciences, University of Salamanca, Paseo de Canalejas, 169, Salamanca 37008, Spain |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Quantum machine learning\u003Cbr>Quantum computing\u003Cbr>Systematic literature review |  | Quantum physics has changed the way we understand our environment, and one of its branches, quantum mechanics, has demonstrated accurate and consistent theoretical results. Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles (photons, electrons, etc.) for subsequent use in performing calculations, as well as for large-scale information processing. These advantages are achieved through the use of quantum features, such as entanglement or superposition. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, scientific challenges are impossible to perform by classical computation due to computational complexity (more bytes than atoms in the observable universe) or the time it would take (thousands of years), and quantum computation is the only known answer. However, current quantum devices do not have yet the necessary qubits and are not fault-tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning, finance, or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a review of the literature published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms used in quantum machine learning and their applications. The methodology follows the guidelines related to Systematic Literature Review methods, such as the one proposed by Kitchenham and other authors in the software engineering field. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms and shows their implementation using computational quantum circuits or ansatzs. The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k-nearest neighbor model, and classical deep learning algorithms, like quantum neural networks. One of the most relevant applications in the machine learning field is image classification. Many articles, especially within the classification, try to solve problems currently answered by classical machine learning but using quantum devices and algorithms. Even though results are promising, quantum machine learning is far from achieving its full potential. An improvement in quantum hardware is required for this potential to be achieved since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential. |  |\n\n1. Introduction  \nCurrently, there is an ongoing challenge in the scientific world to create quantum computers capable of substantial advantages for certain problems compared to conventional computers. The automated tools improvement and methods to assist in the simulation and design of the corresponding applications are required along with the device’s development. Otherwise, one could end up in a situation where one has powerful quantum computers but very few adequate means to use them [1]. On the other hand, scientific progress in materials creation,  \nhardware fabrication, and dis","cbCaiv7qO8y5iUYa","https://ap.wps.com/l/cbCaiv7qO8y5iUYa","pdf",4803551,1,20,"English","en",105,"# Introduction\n## Background and motivation\n## Research aims and scope\n# Quantum computing and quantum machine learning overview\n## Quantum features and computational advantages\n## Current device limitations\n# Systematic literature review methodology\n## Search window (2017–2023)\n## Selection and classification approach\n# Algorithm types and implementations\n## Quantum implementations of classical ML\n## Quantum neural networks and deep learning\n# Applications and research directions","[{\"question\":\"What does quantum computing rely on to achieve computational advantages?\",\"answer\":\"It performs calculations using quantum-mechanical properties such as entanglement and superposition, which can improve time and cost compared with classical approaches.\"},{\"question\":\"Why does quantum machine learning still fall short of its full potential?\",\"answer\":\"Current quantum hardware lacks enough qubits, speed, and scale, and devices are not sufficiently fault-tolerant to fully realize expected benefits.\"},{\"question\":\"How many studies were identified in the systematic literature review, and what was the publication window?\",\"answer\":\"The review identified 94 articles published between 2017 and 2023, analyzing their quantum machine learning techniques and implementations.\"}]","Systematic literature review - 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