Quantum Machine Learning for Anti-Microbial Classification in Essential Oil Components: Challenges and Advancements using QGANs
Anmol Chaure
Quantum machine learning, Generative adversarial networks, Ethnobotany, Bioinformatics
Essential oils have gained widespread recognition for their ability to combat microorganisms, serving as natural alternatives to synthetic antimicrobial agents. However, the intricate nature of essential oil components and their interactions with microorganisms pose challenges in identifying the most potent anti-microbial elements. Traditional screening methods are costly, time-consuming, and yield limited success. This study delves into the potential and limitations of utilizing quantum machine learning in the field of drug discovery. The use of quantum machines stems from the fact that chemical systems are governed by quantum mechanics, which are challenging to simulate with classical computers By leveraging this approach, it becomes feasible to uncover potential anti-microbial agents within essential oils, thus contributing to the development of novel and efficient natural products with antimicrobial properties. This research paper, examines the feasibility of loading essential oil data using Generative Adversarial Networks into quantum machine learning algorithms for classifying the essential oil components based on their anti-microbial properties.
Article Details
Unique Paper ID: 160351

Publication Volume & Issue: Volume 10, Issue 3

Page(s): 209 - 215
Article Preview & Download

Share This Article

Conference Alert


AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management


Last Date: 7th November 2023

Go To Issue

Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews