The legalization of medical cannabis in Morocco’s northern Rif region requires precision agriculture systems capable of supporting highly controlled, traceable and quality-driven cultivation. Medical cannabis is biologically sensitive to micro-variations in soil moisture, vapor pressure deficit (VPD), canopy temperature and nutrient levels, which makes it a demanding testbed for advanced decision-support methods. In this work, we propose and numerically evaluate an end-to-end hybrid quantum–classical framework that combines IoT sensor networks, Sentinel-2 and UAV imagery, GIS integration and quantum-enhanced analytics for regulated medical cannabis cultivation in the Al-Hoceïma region.</p> <p>The framework instantiates three quantum modules: (i) a variational quantum linear solver (VQLS) for Kriging-based spatial interpolation under sparse sensing, (ii) a variational quantum classifier (VQC) for early stress detection from multi-source features, and (iii) a Quantum Approximate Optimization Algorithm (QAOA) for constrained irrigation scheduling. All experiments are conducted on synthetic yet agro-ecologically calibrated data generated for a 4-hectare virtual plot; no real cannabis-field data or quantum hardware are used. In this controlled simulation setting, the quantum-inspired modules achieve moderate improvements over classical baselines (Kriging, Random Forest, neural networks, MILP), for example reducing interpolation RMSE by about 20% and improving early-stress F1-score by several percentage points.</p> <p>We explicitly do not claim hardware-level quantum advantage, nor do we provide a formal proof that VQLS or VQC must outper- form classical Kriging or machine learning in this regime. Instead, the contribution is a transparent formulation and simulation- based assessment of quantum-compatible workflows for precision agriculture in regulated contexts, together with a critical discus- sion of their current limitations and the conditions under which they might become competitive in practice.