Quantum-Inspired Optimization for Large-Scale Socioeconomic and Environmental Decision Problems

Eileen Kladivko

Quantum-Inspired Optimization for Large-Scale Socioeconomic and Environmental Decision Problems

Author(s)	Eileen Kladivko
Country	United States
Abstract	Large-scale socioeconomic and environmental decision problems are characterised by high dimensionality, uncertainty, competing objectives, dynamic constraints and strong interactions among multiple stakeholders. Conventional optimisation methods frequently struggle when decision spaces become combinatorially complex or when economic, social and environmental objectives must be considered simultaneously. Quantum-inspired optimization offers an emerging computational approach that borrows concepts from quantum computing while operating on classical hardware. Rather than requiring fully fault-tolerant quantum computers, quantum-inspired methods employ mathematical representations such as superposition-like states, probabilistic sampling, tunnelling-inspired search and interference-inspired transformations to explore complex solution spaces. This paper examines the potential of quantum-inspired optimization for large-scale socioeconomic and environmental decision-making. It explores applications in resource allocation, energy-system planning, transportation, climate adaptation, supply-chain resilience, land-use planning, public policy and sustainable development. A conceptual framework is proposed that combines quantum-inspired optimisation with machine learning, multi-objective optimisation, uncertainty modelling and decision analytics. Particular attention is given to the challenges of balancing economic efficiency, social equity and environmental sustainability. The paper argues that quantum-inspired approaches should not be considered replacements for established optimisation methods but rather as complementary tools for difficult search and decision problems. Their greatest potential lies in hybrid architectures where quantum-inspired algorithms identify promising regions of large solution spaces and classical optimisation, simulation and human judgement refine the resulting decisions. The study concludes that quantum-inspired optimization could become an important component of next-generation decision-support systems, particularly where conventional approaches face scalability and combinatorial complexity challenges.
Keywords	Quantum-Inspired Optimization, Socioeconomic Decision-Making, Environmental Optimisation, Sustainable Development, Multi-Objective Optimisation, Resource Allocation, Decision Support Systems, Climate Adaptation, Combinatorial Optimisation, Computational Intelligence.
Field	Engineering
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-20

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American Journal of Advanced Multidisciplinary Research and Innovation

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Quantum-Inspired Optimization for Large-Scale Socioeconomic and Environmental Decision Problems

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