Lecture 1: Quantum Computing Applied to Animal Nutrition and Agriculture: Emerging Opportunities and Practical Perspectives.

Abstract Animal nutrition and agricultural systems are defined by complex, interconnected challenges involving biological nonlinearity, uncertainty, highdimensional data, and competing objectives. Although classical computing, statistical modeling, and artificial intelligence have enabled significant advances, many core problems, such as largescale optimization, multivariate prediction, and the integration of heterogeneous biological and production data, become difficult to address as problems scale in size and complexity. Quantum computing is an emerging computational paradigm that leverages quantum mechanical principles to explore solution spaces in fundamentally different ways than classical systems. While quantum hardware is still in an early stage of development, hybrid quantum-classical approaches are beginning to reveal potential applications relevant to both animal nutrition and agriculture, particularly in areas that combinatorial complexity, uncertainty, or computational intractability limit conventional methods. Beyond its theoretical appeal, quantum computing is attracting attention because many agricultural systems problems are inherently combinatorial, dynamic, and multi-objective. Animal nutrition, for example, often requires balancing biological responses, economic constraints, ingredient variability, and environmental outcomes within the same decision framework. These characteristics make the field a useful case study for discussing where quantum-inspired or hybrid quantum-classical methods may eventually offer practical value. This presentation explores quantum computing and what makes it fundamentally different from traditional computing. Key approaches and modalities are introduced, along with their strengths and weaknesses. The talk concludes by examining specific potential uses cases in agriculture and animal nutrition, including ration formulation, resource allocation, systems modeling, and decision support under uncertainty, and how quantum computing may provide an advantage as these fields move toward increasingly data-rich and computationally demanding problems.