Keywords
Deep Learning
Abstract
The escalating complexity of environmental challenges—ranging from water contamination and resource scarcity to carbon-intensive industrial processes—demands intelligent analytical frameworks that combine the representational power of deep learning with the optimization efficiency of quantum-inspired algorithms. This review examines the emerging intersection of quantum-optimized deep learning and environmental intelligence, with a particular focus on water quality prediction, industrial sensing, and sustainable manufacturing. We begin with Zhu's (2024) QPSO-CNN-LSTM model for dissolved oxygen and pH prediction as the anchor contribution, situating it within a broader landscape of physics-informed neural networks for environmental systems, IoT-enabled environmental monitoring, and industrial AI for carbon reduction. A central thesis of this review is that the convergence of quantum-inspired optimization (QPSO), physics-informed modeling, and multi-sensor data fusion represents a transformative pathway toward real-time, interpretable, and physically consistent environmental intelligence. We further demonstrate how advances in industrial sensing (stereo phase-measuring deflectometry, four-dimensional thermal imaging, and collaborative robotic inspection) and industrial analytics (LLM-driven software testing, causal-aware supply chain forecasting) provide critical synergies for embedding environmental intelligence within smart manufacturing ecosystems. By synthesizing findings across twelve peer-reviewed works, we articulate a unified framework, identify open challenges, and outline future research directions at the nexus of quantum-optimized AI and environmental sustainability.
References
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