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Renewable and Sustainable Energy Reviews

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

Akshay Ajagekar, Fengqi You

Published May 26, 2022• 146 citations
Publisher
DOI
10.1016/j.rser.2022.112493

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Because of new technology and machine learning, 2022 became a milestone year in computing history. Such developments have led to a new revolution, which will help address some of the most challenging issues our environment faces. Quantum artificial intelligence (QAI) is able to deal with information and complete computations that are beyond the abilities of traditional systems because it uses ideas unique to quantum mechanics, such as superposition and entanglement. QAI can be proven highly effective in addressing difficult challenges related to resource optimization, the behavior of ecosystems, climate problems, and risks to the environment in environmental management. Using the latest quantum algorithms and parallel computing, QAI can precisely simulate and examine large environmental systems. Because of this, the predictions of environmental phenomena are improved, helping people make more informed decisions toward sustainability. QAI is used in important ways for climate modelling. What distinguishes quantum computers from classical ones is their ability to model atmospheric and oceanic processes in much greater detail. The technology in this field can change how well we predict the effects of climate change. Because of this, global efforts can be better directed to prevent and adapt to the possible effects of climate change. In addition, QAI might significantly change how we think about ecosystems. By relying on quantum neural networks and machine learning, QAI analyzes large volumes of ecological data and discovers connections and relationships that explain what happens inside ecosystems. Because of these insights, we can guide restoration efforts, support conservation initiatives, and ensure sustainable ways of using natural resources to safeguard the balance of nature. The use of QAI also helps in making fair decisions on resource allocation and in lowering environmental impact. Powerful quantum algorithms solve difficult optimization problems much faster, which helps to protect the environment. These algorithms might be applied to help the environment by enhancing how companies move goods, reducing fuel consumption and associated emissions, and increasing the use of renewable energy sources. Ongoing studies are aimed at uncovering every benefit from this technology, despite the fact that QAI for environmental management is still new. These efforts focus on making reliable quantum algorithms, enhancing methods to fix errors in quantum systems, and creating new quantum hardware. People working in environmental science, quantum technology, and legal sectors will need to come together and cooperate to apply theoretical ideas to everyday solutions for the environment. Taking this cooperative strategy will help QAI contribute to the goal of ensuring a healthy and safe Earth for generations to come.
The escalating impacts of climate change and the increasing demand for sustainable development and natural resource management necessitate innovative technological solutions. Quantum computing (QC) has emerged as a promising tool with the potential to revolutionize these critical areas. This review explores the application of quantum machine learning and optimization techniques for climate change prediction and enhancing sustainable development. Traditional computational methods often fall short in handling the scale and complexity of climate models and natural resource management. Quantum advancements, however, offer significant improvements in computational efficiency and problem-solving capabilities. By synthesizing the latest research and developments, this paper highlights how QC and quantum machine learning can optimize multi-infrastructure systems towards climate neutrality. The paper also evaluates the performance of current quantum algorithms and hardware in practical applications and presents realistic cases, i.e., waste-to-energy in anaerobic digestion, disaster prevention in flooding prediction, and new material development for carbon capture. The integration of these quantum technologies promises to drive significant advancements in achieving climate resilience and sustainable development.