Quantum machine learning: Transforming cloud-based AI solutions

Bangar Raju Cherukuri *

Andhra University, INDIA.
 
Research Article
International Journal of Science and Research Archive, 2020, 01(01), 110-122.
Article DOI: 10.30574/ijsra.2020.1.1.0041
Publication history: 
Received on 18 November 2020; revised on 25 December 2020; accepted on 27 December 2020
 
Abstract: 
This study examines the feasibility of placing quantum computing technology into cloud ML systems to make QML far faster and more scalable. Quantum computers tackle standard ML performance challenges through their special traits, including superposition and entanglement. Implementing QML on cloud-based platforms unlocks the specific advantages of scalability and accessibility while providing the required flexibility. Cloud-based systems can better predict results with faster performance when they use quantum algorithms to process machine learning tasks. This research examines how QML connects to cloud computing technology while showing how these industries can use it to handle limited processing power and improve overall system performance.
 
Keywords: 
Quantum Computing; Cloud ML; Quantum Algorithms; QML Speed; Machine Learning; Superposition Entanglement; Quantum Gates; Classical Models; Cloud Systems
 
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Copyright information: 

Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0