Deep architecture; Quantum machine learning; Quantum neural networks
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.
Rai, Pranab; John, Tegil; Dkhar, Josaiah Murfreal
Lecture Notes in Networks and Systems;Volume;1497 LNNS;pp.611-628
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-9184-5_45 https://www.scopus.com/pages/publications/105028364802?origin=resultslist
Rai P., Christ University, Bengaluru, India; John T., Christ University, Bengaluru, India; Dkhar J.M., Christ University, Bengaluru, India