Aquaculture sustainability relies on maintaining dissolved oxygen (DO) levels while minimizing the energy demand of aeration systems. Conventional optimization approaches, including linear programming and heuristic methods, struggle to manage the nonlinear, multi-objective nature of aquaculture environments. To address this challenge, we introduce a quantum-enhanced decision-support framework that integrates the Quantum Approximate Optimization Algorithm (QAOA) with artificial intelligence (AI), machine learning (ML) forecasting, and IoT-enabled Digital Twin simulations.
The proposed architecture consists of three layers: (1) a quantum optimization layer applying QAOA for aeration scheduling, (2) a classical AI layer for real-time prediction of environmental parameters such as temperature, pH, ammonia concentration, and stocking density, and (3) a sensor–actuator layer for continuous monitoring and control of pond conditions. The optimization process is evaluated using six performance indicators: Oxygen Utilization Efficiency (OUE), Fish Oxygen Demand Index (FODI), Ammonia Load Factor (ALF), Aeration Priority Score (APS), Environmental Stress Index (ESI), and Quantum Optimization Impact Factor (QOIF).
Simulation studies demonstrate a 22.5% reduction in daily aeration energy consumption and a 49.8% improvement in DO stability relative to classical optimization techniques, with estimated annual energy savings of $954 per system. Beyond aquaculture, this hybrid approach can be adapted for industrial water optimization applications such as wastewater treatment and smart cooling systems.
This research represents the first implementation of QAOA in aquaculture optimization and highlights its potential to advance quantum-enhanced environmental management. By combining quantum computing, AI, and aquaculture science, the proposed framework offers a scalable and sustainable solution to support global food production systems.