Research work · 2025
Quantum-Enhanced Plant Breeding and Genetic Optimization
Revolutionizing Sustainable Food Production With Quantum Computing
Abstract
The conventional plant breeding has had some limitation in addressing the issues of global food security and challenges. Quantum computing proposes the way of improvement of genetic algorithm based optimization in plant breeding. The study attempts to analyse the impact of quantum computing in the acceleration of plant breeding. The chapter likewise examines the use of quantum algorithms and methods to anticipate complicated traits utilising genome selection and trait mapping. The research also focuses on quantum computing in order to find better gene combination, optimization-based breeding and crop resiliency in the face of the climate changes. Certain areas specifically include quantum acceleration, genome-wide association studies (GWAS) and quantum machine learning for phenotyping prediction and quantum inspired optimization algorithms to design novel breeding schemes. Such perspective paves way for new era to concentrate on improvement of crop and sustainable food production. Such innovation offers something to solve the global food security challenge in the 21st century.