Integrated Quantum Computing and Machine Intelligence for Sustainable Energy Solutions

Integrated Quantum Computing and Machine Intelligence for Sustainable Energy Solutions

Muthuraman Subbiah, R. V. V. Krishna, V. Satyanarayana, Abhinav Kataria
Copyright: © 2024 |Pages: 12
DOI: 10.4018/979-8-3693-4001-1.ch007
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Abstract

The synergistic integration of amount computing and machine intelligence to address challenges in sustainable energy results. By using the computational power of amount computing and the adaptive literacy capabilities of machine intelligence, the study aims to optimize energy product, distribution, and application in a sustainable manner. Through advanced algorithms and optimisation ways, the exploration explores how intertwined amount computing and machine intelligence can enhance the effectiveness, trustability, and environmental sustainability of energy systems. The findings offer perceptivity into the eventuality of this interdisciplinary approach to revise the energy sector, paving the way for the development of innovative results for renewable energy integration, smart grid operation, and energy-effective technologies. Eventually, the exploration contributes to the advancement of sustainable energy results by employing the combined power of amount computing and machine intelligence to address complex challenges in energy optimisation and resource operation.
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The disquisition of integrated amount computing and machine intelligence for sustainable energy results builds upon a foundation of exploration at the crossroad of amount computing, artificial intelligence, and energy systems optimisation (Ranjit, 2014). Several studies have laid the root for this interdisciplinary approach, furnishing perceptivity into the implicit operations, benefits, and challenges of integrating amount computing and machine intelligence in the energy sector. In the field of amount computing, experimenters have made significant strides in developing algorithms and ways for working optimisation problems applicable to energy systems. For illustration, studies by Farhi et al. (2014) and Farhi and Neven (2018) introduced the amount approximate optimisation algorithm (QAOA), a promising approach for working combinatorial optimisation problems applicable to energy grid optimisation and energy resource operation. These algorithms influence amount principles similar as superposition and trap to explore vast result spaces and identify optimal configurations for energy systems.

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