Ongoing Research
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Project 1: Quantum Enhanced Monte Carlo Metropolis Algorithm for Classical Spin Liquid
Collaborators: Kiran Thengil, Arul Anne Elden, Ashish Kumar Singh
Short Description: Developing a quantum-enhanced Monte Carlo Metropolis algorithm to efficiently study classical spin liquid systems.
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Project 2: Quantum-Inspired Optimization for Heat-Stress Management in Large Ruminants
Collaborators: G D Vandana, Kiran Thengil
Short Description: This project aims to develop a quantum-inspired decision-support framework for reducing heat stress in large ruminants such as cattle and buffalo. Sensor and weather data, including temperature–humidity index, rumination, body temperature, water intake, and milk-yield indicators, will be used to estimate heat-stress risk for each animal group. The management problem—when to activate fans, sprinklers, shade, feeding, and water allocation—will be formulated as a QUBO/Ising optimization problem. The objective is to minimize animal heat-stress exposure, production loss, water use, and energy cost under practical farm constraints. The resulting optimization model can be solved using simulated annealing, quantum annealing, QAOA, or quantum-inspired algorithms, providing a climate-resilient precision-livestock strategy.