Climate Change Through Quantum Lens: Computing and Machine Learning

Syed Masiur Rahman

doi:10.1007/s41748-024-00411-2

Climate Change Through Quantum Lens: Computing and Machine Learning

2024-06-04

2

PRE

AI

Sarah Mohammed Al-Judaibi

F

Fahad Saleh Al–Ismail

DOI：10.1007/s41748-024-00411-2

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Quantum computing (QC) is a new approach to perform computations using the principles of quantum mechanics. The demonstration of quantum superiority is a significant landmark in the noisy intermediate scale quantum (NISQ) era. This review critically investigated the possible role of quantum computing in solving or enhancing climate change related studies especially focusing on the expected supremacy in some selected areas. The researchers ascertained a few major areas which could be enhanced through QC including new material and catalyst development and new material production process development through chemical simulation, new optimization techniques especially supporting machine learning, and new modeling approach for fluid dynamics pertinent to atmospheric and oceanic phenomena-key components in climate change research. The contributions in those fields will support better understanding of climate change issues and create enhanced or new climate change mitigation and adaptation opportunities. Quantum algorithms, notably quantum principal component analysis (qPCA), the Harrow, Hassidim, and Lloyd (HHL) algorithm, and least-squares quantum support vector machines (qSVM), offer exponential speedups over classical counterparts in processing large datasets, solving linear systems, and machine learning tasks. Quantum optimization techniques, including quantum annealing and Quantum Approximate Optimization Algorithm (QAOA), demonstrate superior performance in addressing complex optimization problems prevalent in climate change studies. Due to QC's current limitations, like challenges with scaling up, high error rates, and the need for more technological advancements, this review provides a balanced perspective on both the potential and the limitations of using QC to address climate change. In addition, integrating QC with existing computational systems poses challenges related to security vulnerabilities, the need for quantum-safe software, special issues in quantum software engineering, and limitations in variational quantum simulations. Finally, a quantum energy initiative to ensure sustainable quantum technologies with appropriate consideration for energy footprint is essential.

Keyword:

Quantum computing

Machine learning

Quantum machine learning

Climate change studies

Noisy intermediate scale quantum era

Fault-tolerant quantum era

Climate Change Through Quantum Lens: Computing and Machine Learning

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