Seybold Report ISSN: 1533-9211
Manjeet Rai, Sudhanshu Saini, Arvind Dagur
Vol 18, No 3 ( 2023 ) | Licensing: CC 4.0 | Pg no: 194-203 | Published on: 30-03-2023
Abstract
Utilizing quantum mechanics, quantum computing generates giant strides in a way processing in order to tackle clear-cut problems. IBM developed quantum computers to deal with complex problems that even the most sophisticated supercomputers of today can't solve.
Covid-19 has taken over the globe, and it must be the predominant precedence at all levels. We can take precautions and help the infected because we have yet to defeat this virus. Continuous testing is one of the precautions. The goal of this research is to use machine learning advance technologies (like Quantum Computing) to abbreviate the time it clutches to test quantum computers.
We can achieve Covid-19(corona virus disease) detection in unfeigned I-B-M quantum processors and stimulators using the quantum transfer learning method. CT imaging has been recommended by many specialists as a diagnostic modus operandi for Corona virus disease. By rectifying a sample features set, we hope to conduct stratification of Covid-19 and wonted CT (Computer Tomography) images. Due to the better properties of Quantum Computers, this labour must be settled in a mite of the time and with greater accuracy than traditional computers
Keywords:
Covid-19, Variational Quantum Circuit, Quantum Transfer Learnig