Quantum Computing for Sustainable Energy: Optimising SDG-7 Through Smart Energy Management and Financial Planning
Abstract
Sustainable Development Goal 7 (SDG-7) emphasises universal access to affordable, reliable, and modern energy by 2030. Achieving this target requires transformative energy production, distribution, and financial optimisation solutions. This paper investigates the role of quantum computing in advancing SDG-7 objectives, focusing on quantum optimisation algorithms, simulations, and machine learning techniques. By analyzing global and national energy progress indicators, particularly from India, the study identifies areas where quantum algorithms such as Quantum Annealing (QA), Adiabatic Algorithms (AA), QAOA, and Quantum Machine Learning (QML) offer measurable impact in intelligent energy grid optimization, clean energy forecasting, and resource-efficient investment modeling. Further, the paper assesses financial pathways enabled by quantum-enhanced models to support renewable energy rollouts. The results underline quantum computing’s potential to accelerate access, reduce emissions, and maximise economic efficiency in energy systems aligned with SDG-7. This study offers a novel contribution by linking quantum algorithm performance to both energy optimisation and financial outcomes aligned with SDG-7.
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