Environmental and land-use classification is critical in sustainable agricultural planning, as it helps ensure that land resources are managed efficiently, allows for more informed environmental policy decisions, supports urban development regulation, identifies regions suitable for cultivation based on soil and geographic characteristics, aids in resource allocation and conservation planning, fosters data-driven approaches to environmental monitoring, reduces misclassification of land-use patterns that could lead to poor policy outcomes, and increases the accuracy of predictive models used in land management. In this module, we will analyze a soil and land-use dataset by applying a Classical Support Vector Machine (SVM), a Hybrid Quantum Support Vector Classifier (QSVC), and a Variational Quantum Classifier (VQC), where we define parameters such as qubits, feature maps, and ansatz circuits and apply various packages and functions such as Qiskit, ZZFeatureMap, FidelityQuantumKernel, and SPSA.
References:
Barbato, Luigi, Giuseppe Buonaiuto, Lidia Marassi, Stefano Marrone, Carlo Sansone, Massimo Esposito, and Francesco Gargiulo. "Learning to Build Quantum Kernels: A Reinforcement Learning Framework for Quantum SVC Optimization." Preprint, Research Square, March 4, 2026. https://doi.org/10.21203/rs.3.rs-8722632/v1.
IBM Quantum Learning. "Bloch Sphere." In General Formulation of Quantum Information. Accessed March 14, 2026. https://quantum.cloud.ibm.com/learning/en/courses/general-formulation-of-quantum-information/density-matrices/bloch-sphere.
Xiao, Yu. "An Introduction to Hilbert Spaces and the Heisenberg Uncertainty Principle." REU 2017, University of Chicago, August 18, 2017, 1–8.