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Quantum Computing Machine Intelligence for Optimal Battery Performance
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Author(s): Pushpender Sarao (Lovely Professional University, India), R. V. V. Krishna (Aditya College of Engineering and Technology, Jawaharlal Nehru Technological University, Kakinada, India), P. S. Ranjit (Aditya College of Engineering and Technology, Jawaharlal Nehru Technological University, Kakinada, India)and Babu E. R. (Bangalore 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.ch018
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Abstract
This research improves batteries using AI and quantum processing. Quantum computing uses quantum physics to quickly search for many solutions to manage large amounts of data. Deep learning, reinforcement learning, and other machine intelligence use massive datasets to uncover patterns and improve algorithms for quantum computing. To test alternative configurations simultaneously, the authors record operating parameters, ambient variables, and battery attributes in a quantum state. They want to utilize reinforcement learning algorithms to improve charging and draining methods so they operate well and can be used in many situations. This research aims to reduce degradation, improve energy efficiency, and extend battery life. Machine intelligence and quantum computation are used to analyze batteries and optimize performance. Bringing together experts from different sectors could help construct strong, environmentally friendly power networks. This modification may affect energy storage technology greatly. The research's findings could impact electric cars, power grid security, and renewable energy.
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