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- W4361807580 abstract "Even though Quantum Computers are still in their developmental phases, their technological implementation regarding their integration with classical computations is showing fascinating results around the world. Quantum Computers when compared with Classical Computational devices can be interpreted as a ‘candle and light bulb’, two different things accomplishing the same motive. Quantum Computers can provide us with tremendous processing power, which was unforeseen and has led us to question the ways in which they can transform existing technologies even raising concerns for current cryptic methods. Quantum systems have shown great potential in several fields one such is Quantum Machine Learning (QML) which even though a considerably novel field has benefitted from the integration of Quantum algorithms and Machine Learning algorithms, presenting exceptional results from various results. Scientists at Google were able to proclaim Quantum supremacy, this presented the Quantum computer's ability to perform extensively large computations in an extremely short time compared to a Super Computer, this could be beneficial for Machine learning algorithms to process huge amounts of data. Recent usage of a VPN for teleconferencing and a “work from home” scheme during the pandemic has caused a huge surge in network traffic forcing IT infrastructure providers to switch toward Software-Defined Network (SDN), Software Defined Network (SDN) is a prominent technology to provide betterment to the traditional network architecture. Machine Learning and Artificial Intelligence have been used extensively for various aspects of SDN, hence we attempted to explore QML and SDN interactions to assess their ability and the benefits we can achieve from this integration. This manuscript attempts to provide insights on the developments of QML over time and experimenting with SDN to provide a robust and efficient SDN system." @default.
- W4361807580 created "2023-04-05" @default.
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- W4361807580 date "2023-01-01" @default.
- W4361807580 modified "2023-10-13" @default.
- W4361807580 title "Evolution of Quantum Machine Learning and an Attempt of Its Application for SDN Intrusion Detection" @default.
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- W4361807580 doi "https://doi.org/10.1007/978-981-19-9530-9_22" @default.
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