Quantum-Inspired Machine Learning for Chemical Reaction Path Prediction

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Author(s): P. Neelima (School of Engineering and Technology, Sri Padmavati Mahila Visvavidyalayam, India), V. Satyanarayana (Aditya College of Engineering and Technology, Jawaharlal Nehru Technological University, Kakinada, India), K. B. Sravanthi (Loyola Academy, India)and K. Sherin (St. Joseph's Institute of Technology, India)
Copyright: 2024
Pages: 15
Source title: Real-World Challenges in Quantum Electronics and Machine Computing
Source Author(s)/Editor(s): Christo Ananth (Samarkand State University, Uzbekistan), T. Ananth Kumar (IFET College of Engineering, India)and Osamah Ibrahim Khalaf (Al-Nahrain University, Iraq)
DOI: 10.4018/979-8-3693-4001-1.ch020

Keywords: Electrical and Electronics Engineering / Engineering Science Reference / Knowledge Management and Library Science / Quantum Information & Computing

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Abstract

The purpose of this study is to provide a novel method for predicting chemical response routes that is designed to exercise machine literacy techniques inspired by the concept of amount. When it comes to addressing the large number of mechanical interactions that are needed in chemical reactions, traditional types of response path vaticination frequently face obstacles. Within the scope of this investigation, the authors apply the ideas of amount computing in order to create a machine literacy framework that is inspired by amount computing and is developed for the purpose of providing accurate and efficient vaticination of response paths. The solution that has been proposed combines the suggestive power of algorithms that are inspired by amounts with the scalability and versatility of machine literacy models. This framework has been shown to have greater performance in predicting reaction courses when compared to conventional methods. This was demonstrated through extensive testing and confirmation on a variety of chemical systems.

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Quantum-Inspired Machine Learning for Chemical Reaction Path Prediction

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