<p>This study proposes QYieldOpt, a hybrid quantum-classical framework for real-time resource optimization in precision farming, integrating a Quantum Approximate Optimization Algorithm (QAOA-R), Quantum Gradient Allocation Optimizer (QGAO), and quantum algorithm for Sensor Feedback Calibration (QSFC). All results presented in this study are based on simulation experiments using realistic agricultural data sets and quantum circuit emulators. Addressing the classical limitations in dynamic, multi-constraint agricultural environments, the system leverages quantum computing parallelism and ultra-sensitive environmental monitoring using quantum sensor networks (QSNs). QAOA-R solves discrete resource allocation (irrigation valve on/off decisions) via cost Hamiltonian optimization, achieving 89% water utilization and 8492 kg yield in the simulations. QGAO refines continuous variables (fertilizer dosage) using quantum-enhanced gradient descent, reducing resource waste by 30% using penalty-augmented utility functions. QSFC dynamically calibrates utility parameters <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13677_2025_769_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="39" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{a}_{i},\:{b}_{i}\)</EquationSource> </InlineEquation> via quantum sensor data, encoding variables like soil moisture into rotation gates <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13677_2025_769_Article_IEq2.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{R}_{y}\)</EquationSource> </InlineEquation> (<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13677_2025_769_Article_IEq3.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="34" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:\pi\:{s}_{ij}\)</EquationSource> </InlineEquation>) with < 2% spectral error. The closed-loop architecture of the framework enables adaptive adjustments every 15–30 min using real-time QSN feedback. Empirical validation conducted entirely through simulation against classical models (LP, GA, PSO, RL) demonstrated superior performance with 12–18% yield improvements, 22% resource savings, and 4.3 s convergence for 100-zone farms. Under 20% sensor noise, QYieldOpt maintained robustness (R² = 0.919), outperforming classical baselines in terms of accuracy (MAE: 5.41 kg/zone) and scalability (10.6 s for 250 zones). By unifying quantum optimization with high-precision sensing, this study advances sustainable agriculture through energy-efficient resource management, which was validated in simulated and hybrid emulated cloud-edge environments. The modular design ensures theoretical compatibility with existing IoT systems, whereas field trials are essential to establish the practical feasibility of climate-resilient farming. As quantum hardware matures, QYieldOpt paves the way for autonomous and scalable solutions to global food security challenges in the future.</p>

Optimizing resource allocation in precision farming using quantum enhanced algorithms and quantum sensor networks

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2025年9月24日

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Hatoon S. AlSagri,
Ankit Kumar,
Abdul Khader Jilani Saudagar,
Abhishek Kumar,
Linesh Raja

摘要

This study proposes QYieldOpt, a hybrid quantum-classical framework for real-time resource optimization in precision farming, integrating a Quantum Approximate Optimization Algorithm (QAOA-R), Quantum Gradient Allocation Optimizer (QGAO), and quantum algorithm for Sensor Feedback Calibration (QSFC). All results presented in this study are based on simulation experiments using realistic agricultural data sets and quantum circuit emulators. Addressing the classical limitations in dynamic, multi-constraint agricultural environments, the system leverages quantum computing parallelism and ultra-sensitive environmental monitoring using quantum sensor networks (QSNs). QAOA-R solves discrete resource allocation (irrigation valve on/off decisions) via cost Hamiltonian optimization, achieving 89% water utilization and 8492 kg yield in the simulations. QGAO refines continuous variables (fertilizer dosage) using quantum-enhanced gradient descent, reducing resource waste by 30% using penalty-augmented utility functions. QSFC dynamically calibrates utility parameters \(\:{a}_{i},\:{b}_{i}\) via quantum sensor data, encoding variables like soil moisture into rotation gates \(\:{R}_{y}\) ( \(\:\pi\:{s}_{ij}\) ) with < 2% spectral error. The closed-loop architecture of the framework enables adaptive adjustments every 15–30 min using real-time QSN feedback. Empirical validation conducted entirely through simulation against classical models (LP, GA, PSO, RL) demonstrated superior performance with 12–18% yield improvements, 22% resource savings, and 4.3 s convergence for 100-zone farms. Under 20% sensor noise, QYieldOpt maintained robustness (R² = 0.919), outperforming classical baselines in terms of accuracy (MAE: 5.41 kg/zone) and scalability (10.6 s for 250 zones). By unifying quantum optimization with high-precision sensing, this study advances sustainable agriculture through energy-efficient resource management, which was validated in simulated and hybrid emulated cloud-edge environments. The modular design ensures theoretical compatibility with existing IoT systems, whereas field trials are essential to establish the practical feasibility of climate-resilient farming. As quantum hardware matures, QYieldOpt paves the way for autonomous and scalable solutions to global food security challenges in the future.