Quantum Computing’s Role In Addressing Global Sustainability Challenges

Abstract

Due to the growing scale of the sustainability crisis that the world community increasingly experiences in the form of climate change, energy wastefulness, and resource riddles of complex management, cross-sectoral, complex, advanced computational approaches are urgently required to come up with more effective solutions (Steffen et al., 2015). Research in the recent past has brought to attention the possibility of quantum computing in transforming problem solving capacities towards optimization, modeling new climate and in finding new sustainable materials (Cao et al., 2019). In the given paper, the researcher evaluates how quantum computing can assist in addressing sustainability issues through quantum algorithms, hybrid architecture of AI and quantum research, and its ability to demonstrate better performance as compared with its classical counterparts (Preskill, 2018; Biamonte et al., 2017). With the use of variational quantum eigensolvers (VQE), and quantum approximate optimization algorithms (QAOA), we are testing their solutions on the optimization of renewable energy and the simulation of carbon capture materials (Peruzzo et al., 2014). The main findings indicate that quantum-implemented models have the potential of dramatically decreasing the computational complexity and delivering enhanced scalability to extensive data sets, as compared to the current machine learning solutions (Farhi et al., 2014). The implications of our findings are that though quantum computing is currently in its emergent state, its adoption in classical and AI system has potential in promoting the prospects of global sustainability (Raeisi et al., 2021). The next stage should be focused on building more hybrid algorithms and upgrading the hardware to accelerate the achievement of the theoretical potential and translate it into practice (Bharti et al., 2022).

Key Terms: Quantum Computing, Sustainability, Optimization, Climate Modeling, Hybrid

Algorithms, Variational Quantum Eigensolver, QAOA