ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: quantum computing in agricultural sciences: from theory to reality.

Quantum computing (QC) represents a revolutionary paradigm in information processing, leveraging quantum mechanical phenomena (superposition, entanglement, quantum interference, and quantum tunneling) to perform calculations in fundamentally different ways than classical computing (CC). While CC processes information sequentially through Boolean logic operations on discrete binary states (0 s and 1 s), quantum computers manipulate qubits that can exist in superpositions of states, enabling parallel operations on exponentially large state spaces. Despite claims regarding "quantum supremacy," QC remains in its early developmental stages, comparable to the CC of the 1950s and 1960s. True quantum supremacy, where quantum computers demonstrate definitive, practical advantages over classical computers for well-defined tasks, has not yet been established. Practical applications face real challenges, i.e., decoherence, high error rates, and demanding error correction requirements. Three developmental phases are projected: noisy intermediate-scale quantum systems by 2030, broad quantum advantage from 2030 to 2040, and full-scale fault tolerance after 2040. Does QC offer solutions to fundamental problems that classical systems, including supercomputers and artificial intelligence, cannot already resolve? While conventional technologies continue to advance agricultural capabilities through machine learning (ML) and complex optimization, quantum approaches may potentially transform domains that require molecular-level simulations (such as soil chemistry and rumen microbial interactions) or exponentially complex optimization problems in resource allocation. Quantum ML models, such as quantum neural networks, generative adversarial networks, and autoencoders, are being explored in quantum-classical hybrids, which have shown potential for faster optimization and higher-dimensional data representation; but, these advantages remain largely conceptual. The value proposition of QC in agriculture ultimately depends on whether the field's most pressing challenges involve quantum mechanical processes that classical computers cannot simulate efficiently or optimization problems of such complexity that quantum algorithms would provide substantial practical advantages over classical approaches. The agricultural community must also address societal implications, such as access equity, data ownership, algorithmic transparency, and educational preparedness for this emerging technology.