Comparative analysis optical communication based renewable solar cell and quantum network for the reduction of carbon emission

Industrialization, urbanization, population expansion, and changes in lifestyles within the Group of Seven (G7) have raised the danger of global warming since CO₂ emissions directly impact the quantity of power that can be produced from diverse sources. However, the intrinsic energy needs and CO₂ emissions found in renewable energy, especially solar cells and associated equipment, which have been extensively embraced in low-income nations, are seldom, if ever, considered by decision-makers. We propose converting a conventional neural network into a quantum photonic system. First, the classical neurons are made reversible by adding extra bits. After that, unitarity and quantum reversibility are added to the list. This work provides a unique approach to lowering carbon emissions based on environmentally friendly renewable solar cells and environmental thermal image analysis using machine learning architectures. The ambient thermal picture collected from both developed and developing countries was processed using convolutional adversarial Gaussian markov neural networks. The usage of eco-renewable solar cells has led to a reduction in carbon emissions in both industrialized and developing countries. The results of the experiments are broken down into many categories, including prediction accuracy, energy consumption, resilience, execution time, and mean average precision.