Precision agriculture is experiencing a technological transformation driven by artificial intelligence (AI), the Internet of Things (IoT), and big data analytics.Nonetheless, traditional computation models find it difficult to handle large-scale high-dimensional datasets in agriculture.By utilizing phenomena like superposition and entanglement, quantum computing represents a fundamental departure that can greatly accelerate and optimize such algorithms.This paper presents the Hybrid Quantum Optimization and Machine Learning Framework (HQOMLF), which comprises its derivatives, such as the Quantum-Enhanced Resource Allocation (QERA) and Quantum Convolutional Neural Networks (QCNN) for effective resource distribution and predictive analytics.The proposed method outperforms classical models with respect to computation speed, accuracy, and scalability.It demonstrates via experiments that processing speed can be improved up to 3.5× and resource optimization of SBT can be enhanced by 11.3% using the proposed method.Based on data analysis, there are several unsolved problems, like hardware limitations and complexity of the algorithm, but quantum computing in agronomics has well-picking potential.These results set the stage for future quantum-classical hybrid models towards sustaining, data-driven agricultural solutions.
Precision agriculture is experiencing a technological transformation driven by artificial intelligence (AI), the Internet of Things (IoT), and big data analytics.Nonetheless, traditional computation models find it difficult to handle large-scale high-dimensional datasets in agriculture.By utilizing phenomena like superposition and entanglement, quantum computing represents a fundamental departure that can greatly accelerate and optimize such algorithms.This paper presents the Hybrid Quantum Optimization and Machine Learning Framework (HQOMLF), which comprises its derivatives, such as the Quantum-Enhanced Resource Allocation (QERA) and Quantum Convolutional Neural Networks (QCNN) for effective resource distribution and predictive analytics.The proposed method outperforms classical models with respect to computation speed, accuracy, and scalability.It demonstrates via experiments that processing speed can be improved up to 3.5× and resource optimization of SBT can be enhanced by 11.3% using the proposed method.Based on data analysis, there are several unsolved problems, like hardware limitations and complexity of the algorithm, but quantum computing in agronomics has well-picking potential.These results set the stage for future quantum-classical hybrid models towards sustaining, data-driven agricultural solutions.
Changes in Weather decrease in soil fertility, limited water resources and growing populations all are making agriculture harder. Even though precision agriculture uses traditional machine learning techniques, the majority of these approaches are unable to effectively handle the extremely complex and multidimensional data coming from crop genetics, weather systems, soil sensors, and satellite surveillance. In this regard, the current research presents a framework for intelligent agriculture that is based on quantum machine learning and quantum optimization. The system can represent and analyze the agricultural data far more efficiently than traditional computer methods by taking advantage of quantum phenomena like superposition and entanglement. Improved crop yield prediction and intelligent resource management via quantum-inspired optimization, particularly QAOA, are the two main objectives of the work. The model achieves accuracy values for yield estimates between 70 and 90%, according to simulation results, and it allows for real-time modification of fertilizer and irrigation. Lastly, it provides farmers with an easy-to-use dashboard to visualize insights. The current study provides a conceptual and technical foundation for quantum computing applications in agriculture in the near future, for more resource-efficient, data-driven, and sustainable farming, even though current quantum hardware is still in the development stage.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
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.
Background: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed predictive analytics across domains such as healthcare, finance, and environmental sciences. However, traditional ML models face limitations when dealing with large-scale, high-dimensional datasets, particularly due to computational inefficiencies and the "curse of dimensionality." Quantum computing offers promising solutions by leveraging superposition and entanglement to perform complex computations at exponential speeds. Objective: This study aims to explore how quantum computing can enhance AI and ML models, particularly in the domain of predictive analytics. It proposes a comprehensive Quantum-AI framework that integrates quantum technologies into existing predictive systems to overcome the challenges posed by classical approaches. Methods: The study employed hybrid quantum-classical models using frameworks like Qiskit, TensorFlow Quantum, and Pennylane. Datasets were sourced from real-world applications in finance, healthcare, and climate science. Experiments were designed to compare classical and quantum- enhanced models on metrics such as accuracy, scalability, and computational time. Specific algorithms used include Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN). Result: The proposed quantum-enhanced models showed significant improvements: • Accuracy increased from 85% (classical) to 92% (quantum) in finance-related predictions. • Diagnostic models in healthcare achieved a 94% accuracy rate and reduced training time by 50%. • Climate modeling tasks achieved 90% improved accuracy and 20% faster simulation times. The results validate the effectiveness of quantum models in processing large, complex datasets more efficiently. Conclusion: Quantum-enhanced AI presents a transformative approach to predictive analytics by addressing the core limitations of classical ML systems. This study demonstrates the practical feasibility and industrial relevance of integrating quantum computing into AI workflows. It opens avenues for further research in developing scalable, domain-specific quantum algorithms and hybrid AI models for real-world applications.
Smart agriculture has emerged as a transformative approach to enhance crop productivity, resource efficiency, and environmental sustainability using advanced sensing and computational technologies. Wireless Sensor Networks (WSNs) enable real-time monitoring of agricultural parameters such as soil moisture, temperature, humidity, and crop health. However, traditional machine learning models struggle to efficiently process high-dimensional, noisy, and heterogeneous agricultural sensor data. This study proposes a Wireless Sensor-Driven Smart Agriculture framework using Scalable Quantum Convolutional Models (SQCM). The proposed model integrates wireless sensor data acquisition with quantum-inspired convolutional neural networks to enhance pattern recognition, predictive accuracy, and scalability. The quantum convolutional layers enable high-dimensional feature representation, while classical layers ensure efficient decision-making for agricultural optimization. The model is evaluated using agricultural sensor datasets, and performance is measured using accuracy, precision, recall, and RMSE for environmental prediction tasks. Experimental results demonstrate that the proposed quantum-enhanced architecture outperforms traditional machine learning and deep learning models in crop condition prediction and resource optimization. The framework is suitable for precision agriculture, automated irrigation systems, and smart farming applications.
Quantum computing and artificial intelligence (AI) are positioned as groundbreaking tools capable of tackling complexities inherent to modern computation posed by high-dimensional optimization problems.This research presents a quantum-enhanced optimization framework that leverages the QAOA (Quantum Approximate Optimization Algorithm) and VQE (Variational Quantum Eigen solver) within a hybrid quantum-classical paradigm.Machine learning models, including reinforcement learning and NN (neural networks), are integrated to optimize the quantum circuit parameters, enhance convergence, and improve scalability for complex problem spaces.Experimental evaluations demonstrate that this approach outperforms traditional optimization techniques in terms of efficiency and solution accuracy, particularly for large-scale multi-objective tasks.The framework is applied to key domains such as smart energy systems, logistics, and scientific modeling, illustrating its versatility and alignment with sustainable development goals.This study also explores the limitations of current quantum hardware and discusses future pathways for advancing hybrid quantum AI systems, offering significant contributions to next generation computational methodologies and intelligent problem solving.