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.
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.
Abstract Quantum computing (QC) is not a futuristic notion in agriculture, though its full potential has yet to be realized. QC is an emerging field at the intersection of physics and computer science that holds immense potential to revolutionize various sectors, including agriculture production and artificial intelligence (AI) modeling. While QC is still in the early stages of development and practical applications within agriculture are not yet widespread, researchers are actively exploring its potential benefits in various agricultural domains, including crop optimization, livestock breeding, and environmental monitoring. QC harnesses the principles of quantum mechanics to perform computations using quantum bits or qubits, which can exist in multiple states simultaneously. Unlike classical computers, which rely on binary bits representing 0 or 1, quantum computers exploit phenomena such as superposition and entanglement to process information in parallel, potentially offering exponential speedup for certain types of problems. In agriculture production, particularly in animal science, QC offers promising avenues for optimizing processes and enhancing productivity. Quantum algorithms can analyze vast amounts of genomic data to improve breeding programs, leading to the development of more resilient and productive livestock breeds. Furthermore, QC can facilitate precision farming techniques by modeling complex environmental factors and animal behavior to optimize feeding strategies, disease management, and overall farm management practices. Moreover, QC can significantly benefit AI modeling by accelerating computations and enabling more efficient training of AI models. Quantum algorithms can enhance the performance of AI algorithms in various tasks, including pattern recognition, natural language processing, and predictive analytics. By leveraging quantum-enhanced optimization algorithms, AI models can achieve better convergence and accuracy, leading to more effective decision-making and problem-solving capabilities. While hybrid intelligent models also represent a novel frontier in agriculture, QC has the potential to expedite the merging of mechanistic and AI modeling paradigms, facilitating a more holistic understanding of complex systems in agriculture and beyond. By integrating mechanistic models, which describe the underlying physical processes, with AI models, which learn patterns from data, quantum computing can enable comprehensive simulations and predictions of agricultural systems. This fusion of modeling paradigms can lead to more accurate and robust predictions of crop yields, livestock performance, and environmental impacts, facilitating informed decision-making for farmers and policymakers. The application of QC in agriculture, however, requires interdisciplinary collaborations between physicists, computer scientists, agronomists/animal scientists, and AI researchers. These collaborations can drive the development of quantum algorithms tailored to agricultural applications, the integration of quantum-enhanced AI techniques into existing modeling frameworks, and the deployment of QC resources in real-world agricultural systems. Ultimately, harnessing the power of QC holds the potential to revolutionize agriculture production practices, including regenerative agriculture, and advance AI modeling capabilities, paving the way for a more sustainable and efficient agricultural industry.
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.
The innovations of quantum computing, sophisticated tools, and artificial intelligence (AI) are changing the future of sustainable farming to present solutions to resource management, precise care, and environment monitoring of crops and animals. This section talks about how the application of quantum computing transforms agricultural data analysis, optimization, and simulation processes. It highlights that the integration of the IoT sensors, quantum-enhanced AI system models, and heterogeneous modeling methods makes it manageable and automates processes in the farms. The second portion of the chapter discusses the application of AI in crop prediction, identification of diseases and pests, examination of livestock DNA, and irrigation systems management. Despite some remaining hardware and integration problems, quantum–classical and multi-disciplinary science cooperation/collaboration will most likely lead to greater scalability, efficiency, and sustainability in agriculture. Adopting the most recent technologies in the agricultural production industry can increase production, reduce damage to the environment, and guarantee the availability of food as the global demands evolve.
With increasing computational demands in agriculture and life sciences, quantum computing is emerging as a potential alternative to classical computing. Unlike classical computers, which utilize binary bits, quantum computers utilize quantum bits (qubits) with unique properties such as superposition and entanglement, enabling them to solve certain computational problems more efficiently and achieve significant speed-ups in specific applications. In this manuscript, we evaluate the potential of quantum computing in agriculture and life sciences by reviewing computational challenges suitable for quantum computing and exploring exemplary domain applications. We examine optimization problems in agrifood supply chains, large-scale linear equation systems in animal breeding, quantum-based network architectures for machine learning in classifying satellite images for land-use analysis, quantum simulations for resource recovery from agriculture waste streams, and quantum search algorithms for genome assembly. Each computational problem type presents unique opportunities and challenges, underscoring the need for tailored quantum algorithms. Furthermore, we provide a critical assessment of the broader potential of quantum computing, discussing its challenges, limitations, and how to facilitate a potential implementation. While current quantum hardware remains limited, developing quantum algorithms is still valuable — not only to prepare for future advancements but also to foster innovation through interdisciplinary collaboration. Rather than replacing traditional computing, we foresee quantum computing complementing classical systems, offering novel solutions to previously intractable problems. Continued research and interdisciplinary collaborations are essential to realize the full potential of quantum computing, paving the way for pioneering advancements in agriculture and life sciences.
Quantum computing, an emerging paradigm in computational technology, holds the potential to revolutionize agricultural optimization by solving complex problems that are currently infeasible for classical computers. This chapter explores the application of quantum computing in key areas of agriculture, including crop yield optimization, climate modeling, supply chain efficiency, and resource allocation. By leveraging quantum algorithms such as Quantum Annealing, Variational Quantum Eigensolvers (VQE), and Quantum Machine Learning, agricultural processes can be optimized to enhance productivity, reduce waste, and minimize environmental impact. The integration of quantum computing with existing technologies like the Internet of Things (IoT) and artificial intelligence (AI) promises to accelerate the transition towards smart farming, enabling predictive analytics and real-time decision-making on an unprecedented scale.
Modern agriculture generates large, heterogeneous and high-dimensional data streams whose volume and combinatorial complexity increasingly strain classical computation. Quantum and quantum-inspired computing have been proposed as a means of representing and searching such spaces more efficiently. This review synthesises the emergence of quantum-inspired artificial intelligence and computing in agriculture, maps where these methods have been applied, appraises the certainty of the reported evidence, and evaluates the conditions under which the methods are likely to add value over classical approaches. Following the PRISMA 2020 guideline, four bibliographic databases (Scopus, Web of Science, IEEE Xplore and PubMed) were searched for peer-reviewed studies published between 2017 and 2026, supplemented by citation chaining and a constrained grey-literature scan from which preprints reporting a fully specified method and result were also eligible. After removal of 149 duplicates, 478 records were screened on title and abstract, 82 were assessed in full text, and 17 studies met the eligibility criteria and were carried into a thematic, task-oriented synthesis; no quantitative meta-analysis was performed because reported outcomes are not commensurable. The 17 included studies concentrate in five domains including the quantum-inspired optimisation (2 studies), quantum machine learning (5), plant disease detection (3), genomics and bioinformatics (6), and precision-agriculture sensing (1). By task, the corpus clusters on classification (7 studies) and sequence assembly/alignment (6), with optimisation (2), clustering (1) and sensing (1) thinly represented, and prediction/regression and dynamic, time-resolved phenotyping absent (0 studies each). Reported gains are frequently large in headline terms. For example, quantum image-descriptor disease classifiers reporting accuracies near 99% on curated leaf datasets but rest predominantly on simulators or noisy intermediate-scale quantum (NISQ) hardware, curated benchmarks and weak classical baselines, so the certainty of any quantum advantage is low. Quantum-inspired methods are a credible near-term toolset that can run on classical hardware while quantum processors mature, but current evidence is simulation-heavy, weakly baselined and difficult to reproduce. The clearest research gap is dynamic disease and phenotype-progression modelling. The paper outline a roadmap toward Agriculture 5.0, quantum-enabled breeding and quantum phenomics that prioritises temporal, uncertainty-aware modelling supported by stronger baselines and field validation.