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Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality

Reference Type: 

Journal Article

Ajagekar, Akshay, and Fengqi You. 2022. “Quantum Computing and Quantum Artificial Intelligence for Renewable and Sustainable Energy: A Emerging Prospect towards Climate Neutrality.” Renewable and Sustainable Energy Reviews 165 (September): 112493.

Transitioning from fossil fuels to renewable sources and developing sustainable energy materials for energy production and storage are critical factors in achieving climate neutrality. These can be realized through innovative strategies to provide viable, economically competitive, and scalable technologies ranging across various sectors. Quantum computing (QC) has the potential to revolutionize various domains of science and engineering, including macro-energy systems and sustainable energy materials design. Conventional approaches for renewable and sustainable energy systems solely rely on classical computing techniques that may not scale well with the increasing size and complexity of applications. Owing to the advancements in quantum hardware and algorithms, QC and quantum artificial intelligence make promising tools to handle renewable and sustainable energy systems even at larger scales. In this review, we discuss the prospects of QC for various areas of applications in energy sustainability to help address climate change. In addition to providing a brief background on the operations of quantum computers, the constituent segments of widely adopted QC-based techniques that improve the computational efficiency of quantum chemistry calculations for sustainable energy materials along with quantum artificial intelligence methods that can address complex optimization and machine learning problems arising in renewable energy systems are also introduced in this paper. We screen the presented quantum algorithms based on their performance on current quantum devices despite their promising potential. Furthermore, sustainable energy applications that may draw advantages from QC-based strategies are identified in this work while simultaneously setting realistic expectations over the potential improvements offered over classical techniques.

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