Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Rai, Pranab; John, Tegil; Dkhar, Josaiah Murfreal Title A name given to the resource The Quantum Shift inMachine Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1497 LNNS;pp.611-628 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-9184-5_45" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-9184-5_45</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028364802?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028364802?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rai P., Christ University, Bengaluru, India; John T., Christ University, Bengaluru, India; Dkhar J.M., Christ University, Bengaluru, India Description An account of the resource Quantum machine learning (QML) leverages the power of quantum computing to extensively enhance the performance of simple ML algorithms. This kind of enhancement efficiently avoids complicated high-dimensional and computationally expensive applications. Following exciting discoveries of some key algorithms such as Grovers and Shors algorithm, QML came up with advanced models such as quantum neural networks (QNNs), quantum support vector machines (QSVMs), and quantum deep architecture. The discussed models give better results in data processing, pattern recognition, and optimization in comparison to their classical variants. This survey discusses the most important developments in QML with focus on Quantum Fourier Transform for encoding the data. Applications of QML in quantum chemistry, secure distributed computing, and high-dimensional data analysis are shown. It will present new breakthroughs and continue to challenge practical, scalable QML solutions toward mapping out the course toward quantum advantage in machine learning tasks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Deep architecture; Quantum machine learning; Quantum neural networks Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981969183-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online