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Machine Learning-Guided Optimization of Chemical Processes Using Quantum Computers
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Author(s): M. Sunil Kumar (Mohan Babu University, India), V. Satyanarayana (Aditya College of Engineering and Technology, Jawaharlal Nehru Technological University, Kakinada, India), T. Nagalakshmi (SRM TRP Engineering College, India)and V. V. S. Sasank (Koneru Lakshmaiah Education Foundation, India)
Copyright: 2024
Pages: 14
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.ch010
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Abstract
In order to develop new optimisation tactics for chemical responses, the purpose of this work is to make use of the processing capacity of ultramodern computers and the prophetic powers of machine literacy algorithms. The purpose of this work is to probe the implicit to speed up response discovery, ameliorate response yields, and drop energy consumption. This is fulfilled by the integration of quantum computing simulations and machine literacy- guided methodologies. To develop algorithms and ways that exploit the amount nature of calculating to break optimization problems essential in chemical processes. To use machine literacy styles to enhance the effectiveness and effectiveness of these amount algorithms. Quantum computers have the eventuality to exponentially speed up certain types of optimization problems compared to classical computers. This includes tasks similar as bluffing molecular structures, prognosticating chemical responses, and optimizing response conditions.
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