Abstract Optimization of precision agriculture, a combinatorial optimization problem that has both dependent and independent binary variables associated with selecting the one-to-one mapping of crop planting, irrigation, fertilization, and land management practices will scale poorly as farm size increases. The recently proposed standard Quantum Approximate Optimization Algorithm (QAOA) falls into the category of hybrid quantum classically based optimization but has slow convergence rates and has been shown to become trapped in local minima through two p independent, static optimization variables with no run-time guidance. To address this, we propose an Adaptive Quantum Approximate Optimization Algorithm (A-QAOA) that implements a feedback-driven, layer-wise parameter scheduling approach based upon two adaptive runtime metrics of cost function change (C) and solution variance (Var(C)). A-QAOA improves convergence rates without compromising the optimum solution by reducing the classical optimizer's search space from O(p) — representing 2p independent variables — to just 2 adaptive base variables γ₀ and β₀. A-QAOA will provide a faster convergence rate (72.5%) and shorter runtime (72.1%) compared to the standard QAOA when tested within Qiskit Aer using the COBYLA optimization algorithm across five independent graph instances (10 trials/graph for 50 total trials per algorithm). Specifically, the A-QAOA converged 72.5% faster compared with the standard QAOA (95.4→26.2 iterations; t = 78.53, p < 0.0001, Cohen's d = 15.7) and exhibited a run-time reduction of 72.1% (30.0s→8.4s; t = 28.05, p < 0.0001, d = 5.6).To our knowledge, this is the first work utilizing runtime feedback based adaptive QAOA parameter scheduling methods for resource allocation in the field of precision agriculture and for demonstrating benchmark quantum-enhanced precision agriculture site optimization using NISQ (Noisy Intermediate-Scale Quantum) technology.
Abstract Optimization of precision agriculture, a combinatorial optimization problem that has both dependent and independent binary variables associated with selecting the one-to-one mapping of crop planting, irrigation, fertilization, and land management practices will scale poorly as farm size increases. The recently proposed standard Quantum Approximate Optimization Algorithm (QAOA) falls into the category of hybrid quantum classically based optimization but has slow convergence rates and has been shown to become trapped in local minima through two p independent, static optimization variables with no run-time guidance. To address this, we propose an Adaptive Quantum Approximate Optimization Algorithm (A-QAOA) that implements a feedback-driven, layer-wise parameter scheduling approach based upon two adaptive runtime metrics of cost function change (C) and solution variance (Var(C)). A-QAOA improves convergence rates without compromising the optimum solution by reducing the classical optimizer's search space from O(p) — representing 2p independent variables — to just 2 adaptive base variables γ₀ and β₀. A-QAOA will provide a faster convergence rate (72.5%) and shorter runtime (72.1%) compared to the standard QAOA when tested within Qiskit Aer using the COBYLA optimization algorithm across five independent graph instances (10 trials/graph for 50 total trials per algorithm). Specifically, the A-QAOA converged 72.5% faster compared with the standard QAOA (95.4→26.2 iterations; t = 78.53, p < 0.0001, Cohen's d = 15.7) and exhibited a run-time reduction of 72.1% (30.0s→8.4s; t = 28.05, p < 0.0001, d = 5.6).To our knowledge, this is the first work utilizing runtime feedback based adaptive QAOA parameter scheduling methods for resource allocation in the field of precision agriculture and for demonstrating benchmark quantum-enhanced precision agriculture site optimization using NISQ (Noisy Intermediate-Scale Quantum) technology.
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 ai,bi via quantum sensor data, encoding variables like soil moisture into rotation gates Ry ( πsij ) 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.
Efficient irrigation scheduling is a major challenge in precision agriculture due to increasing water scarcity, climate variability, and growing agricultural demand. Conventional irrigation strategies based on fixed thresholds, heuristic rules, or classical optimization techniques often suffer from scalability limitations, inefficient resource allocation, and poor adaptability under dynamic environmental conditions. This paper presents a hybrid quantum-classical optimization framework for intelligent irrigation scheduling using the Quantum Approximate Optimization Algorithm (QAOA). The irrigation decision problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model integrating soil moisture conditions, irrigation constraints, and water allocation objectives. The proposed framework employs variational quantum optimization to determine optimal irrigation decisions while minimizing unnecessary water consumption. Experimental evaluations conducted using Qiskit-based simulations demonstrate that the proposed QAOA framework successfully generates irrigation decisions fully consistent with classical threshold-based optimization for the evaluated dataset. Comprehensive visualization analyses including soil moisture distribution, irrigation decision mapping, threshold boundary analysis, and water utilization comparison validate the correctness and interpretability of the proposed optimization mechanism. Although the current experimental setup primarily serves as a proof-of-concept validation, the results indicate strong scalability potential for future large-scale agricultural systems involving complex multi-objective optimization constraints. The proposed framework establishes a foundational architecture for future quantum-enabled precision agriculture systems integrating IoT sensing, real-time environmental monitoring, and intelligent resource optimization for sustainable smart farming applications.
The transition from theoretical quantum computing to practical real-world applications heavily relies onhybrid quantum-classical architectures designed for NoisyIntermediate-Scale Quantum (NISQ) devices. Among these approaches, the Quantum Approximate Optimization Algorithm(QAOA) is a prominent strategy for addressing complex combinatorial optimization challenges, such as the NP-hard MaxCut problem. Instead of performing the entire computationon a quantum processor, QAOA utilizes a quantum circuit toevaluate a parameterized cost function, whereas a classical optimizer refines the parameters iteratively. This study presentsa rigorous empirical evaluation comparing the performanceof QAOA against established classical solvers on small-scaleMax-Cut instances. All experiments were implemented andexecuted using Python (Qiskit Aer) on a state-vector simulator.Using Erdős–Rényi random graphs and regular graphs with4 to 20 nodes, we benchmark QAOA at depths p = 2 andp = 3 against simulated annealing, the Goemans–WilliamsonSDP-based algorithm, and a linear-time greedy heuristic. Ourexperimental results demonstrate that low-depth QAOA implementations exhibit severe performance degradation, prohibitive computational overhead (O(2^n) scaling), and low convergencerates compared to mature classical algorithms. At 20 nodes,QAOA (p = 2) achieves only a 0.467 approximation ratio versus0.856 for Goemans–Williamson, with a 210× runtime slowdownover greedy heuristics
Hybrid Quantum–Classical Optimization combines the advantages of quantum computing and classical optimization to address complex combinatorial optimization problems. The Quantum Approximate Optimization Algorithm (QAOA), as an outstanding Variational Quantum Algorithm (VQA), shows great potential in solving combinatorial optimization problems. Classical optimization methods play a crucial role in tuning the parameters of quantum circuits, affecting the algorithm’s convergence speed and solution quality. This paper designs and simulates QAOA circuits based on the Qiskit framework, focusing on comparing the performance of three classical optimizers— Adaptive Moment Estimation (Adam), Constrained Optimization BY Linear Approximations (COBYLA), and Simultaneous Perturbation Stochastic Approximation (SPSA)—in parameter optimization. Adam and SPSA perform more stably in deeper circuits, making them suitable for high-dimensional parameter spaces, whereas COBYLA tends to get trapped in local optima and exhibits greater fluctuations under limited iterations. The results further highlight the importance of classical optimizer selection and hyperparameter tuning in enhancing the performance of hybrid quantum–classical optimization algorithms.
Efficient Job-Shop Scheduling (JSS) is still a challenging combinatorial optimisation problem in engineering systems because of machine-capacity constraints, operation precedence requirements and the exponential growth of the search space for the scheduling problem. The Quantum Approximate Optimization Algorithm (QAOA) has been shown to be a promising approach to solving these optimisation problems, however, when using realistic quantum noise models, the scheduling quality and reliability of the solution can be compromised. This work presents a noise-adaptive QAOA framework that automatically scales the number of shots measured, the classical optimiser, and the circuit depth, informed by an estimated quantum-noise level, and adaptively uses different error mitigation strategies. The scheduling problem is then formulated as a Quadratic Unconstrained Binary Optimisation (QUBO) problem and tested with small-scale engineering benchmark instances, under various simulated noise circumstances. Comparative simulation results show that the proposed adaptive strategy is able to keep the optimality gap low and feasible-solution rate high in every instance, and achieve competitive scheduling performance as compared to a fixed-depth noisy QAOA. The study shows how the adaptive selection of quantum parameters can enhance the robustness of QAOA for small systems of engineering scheduling problems in the presence of noisy quantum devices.
The quantum approximate optimization algorithm (QAOA) is a hybrid variational quantum-classical algorithm that solves combinatorial optimization problems. While there is evidence suggesting that the fixed form of the standard QAOA ansatz is not optimal, there is no systematic approach for finding better ansätze. We address this problem by developing an iterative version of QAOA that is problem-tailored, and which can also be adapted to specific hardware constraints. We simulate the algorithm on a class of Max-Cut graph problems and show that it converges much faster than the standard QAOA, while simultaneously reducing the required number of CNOT gates and optimization parameters. We provide evidence that this speedup is connected to the concept of shortcuts to adiabaticity.