Faculty Publications

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Author: search.filters.author.Chaudhary, S.

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    Receiver architectures for 5g: Current status and future prospects
    (Springer Science and Business Media Deutschland GmbH, 2021) Kumar, A.; Sengar, B.S.; Chaudhary, S.; Pandey, S.K.; Pandey, S.K.; Hasan Raza Ansari, M.; Aaryashree, A.
    In this chapter, the recent progress in receiver architecture and various aspects of the available receiver architectures have been discussed. Besides, an overview of the systematic classification of architecture has been analyzed. Documentation of new possibilities and system-level trade has been closely inspected. Certainly, there is a requirement of low-power, flexible, and high-performance receiver architecture for the successful implementation of the 5G network. Different works in this regard have been considered as examples for discussing the status and prospects of architectures with respect to 5G future. Various architectures considered in this chapter can be very valuable to design 5G network in future and will expose the research community with new possibilties to explore for further improvements. © Springer Nature Singapore Pte Ltd. 2021.
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    Quantum Machine Learning: A Review and Current Status
    (Springer Science and Business Media Deutschland GmbH, 2021) Mishra, N.; Kapil, M.; Rakesh, H.; Anand, A.; Mishra, N.; Warke, A.; Sarkar, S.; Dutta, S.; Gupta, S.; Prasad Dash, A.; Gharat, R.; Chatterjee, Y.; Roy, S.; Raj, S.; Kumar Jain, V.; Bagaria, S.; Chaudhary, S.; Singh, V.; Maji, R.; Dalei, P.; Behera, B.K.; Mukhopadhyay, S.; Panigrahi, P.K.
    Quantum machine learning is at the intersection of two of the most sought after research areas—quantum computing and classical machine learning. Quantum machine learning investigates how results from the quantum world can be used to solve problems from machine learning. The amount of data needed to reliably train a classical computation model is evergrowing and reaching the limits which normal computing devices can handle. In such a scenario, quantum computation can aid in continuing training with huge data. Quantum machine learning looks to devise learning algorithms faster than their classical counterparts. Classical machine learning is about trying to find patterns in data and using those patterns to predict further events. Quantum systems, on the other hand, produce atypical patterns which are not producible by classical systems, thereby postulating that quantum computers may overtake classical computers on machine learning tasks. Here, we review the previous literature on quantum machine learning and provide the current status of it. © 2021, Springer Nature Singapore Pte Ltd.