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Quantum-Enhanced Land Classification for Reforestation using Quantum Machine Learning and GIS

Satyam Shrivastav, Varad Trimbakkar, Purav Shah, N. H. More

Published May 31, 2026• 0 citations
Publisher

Abstract

Abstract This work investigates the feasibility of using quantum machine learning for reforestation-suitability classification in the Mumbai region by integrating Quantum Support Vector Machines (QSVMs) with a GIS-based preprocessing pipeline. Satellite-derived indicators---NDVI, rainfall, slope, soil pH, and soil organic carbon---are extracted from Google Earth Engine and labeled using rule-based ecological thresholds. The resulting dataset is standardized, scaled to \([-1, 1]\), balanced with SMOTE, and compressed using Principal Component Analysis (PCA) to align the feature dimension with the number of qubits. Two quantum kernel models, based on Pauli and ZZ feature maps, are implemented in Qiskit and evaluated against classical SVM baselines under identical preprocessing conditions. On the balanced dataset, the classical SVM achieves an accuracy of approximately 98\%, while Pauli- and ZZ-based QSVMs obtain 89\% and 77\% accuracy, respectively. These results indicate that, although quantum models do not surpass classical SVMs in this setting, well-designed quantum kernels can achieve competitive performance on small, structured geospatial datasets. The study positions QSVMs as complementary tools rather than replacements for classical models and highlights the importance of scaling, class balancing, and PCA--qubit alignment for stable quantum behavior in environmental analytics.

DOI
10.21203/rs.3.rs-8212360/v1

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Abstract This work investigates the feasibility of using quantum machine learning for reforestation-suitability classification in the Mumbai region by integrating Quantum Support Vector Machines (QSVMs) with a GIS-based preprocessing pipeline. Satellite-derived indicators---NDVI, rainfall, slope, soil pH, and soil organic carbon---are extracted from Google Earth Engine and labeled using rule-based ecological thresholds. The resulting dataset is standardized, scaled to ([-1, 1]), balanced with SMOTE, and compressed using Principal Component Analysis (PCA) to align the feature dimension with the number of qubits. Two quantum kernel models, based on Pauli and ZZ feature maps, are implemented in Qiskit and evaluated against classical SVM baselines under identical preprocessing conditions. On the balanced dataset, the classical SVM achieves an accuracy of approximately 98%, while Pauli- and ZZ-based QSVMs obtain 89% and 77% accuracy, respectively. These results indicate that, although quantum models do not surpass classical SVMs in this setting, well-designed quantum kernels can achieve competitive performance on small, structured geospatial datasets. The study positions QSVMs as complementary tools rather than replacements for classical models and highlights the importance of scaling, class balancing, and PCA--qubit alignment for stable quantum behavior in environmental analytics.
Quantum Machine Learning (QML) has emerged as an active area of research, yet its practical readiness for real-world geospatial applications remains uncertain due to constraints related to data availability, preprocessing requirements, and current quantum hardware limitations. This study evaluates the feasibility and behaviour of quantum kernel-based learning models within a realistic GIS workflow, using land suitability analysis for reforestation in the Mumbai metropolitan region as a representative case study although demonstrated using the Mumbai metropolitan region as a case study, the proposed GIS-driven preprocessing pipeline and quantum kernel evaluation framework are transferable to other regions with comparable remote-sensing data availability and environmental indicators. A geospatial dataset is constructed from satellite-derived environmental variables, including Normalized Difference Vegetation Index (NDVI), annual rainfall, terrain slope, soil pH, and soil organic carbon, with suitability labels assigned using ecologically motivated rule-based thresholds that serve as heuristic ground truth. The dataset is processed through a standardized pipeline involving feature scaling, Principal Component Analysis (PCA), stratified train–test splitting, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Quantum Support Vector Machines (QSVMs) employing Pauli and ZZ feature maps are implemented using Qiskit and evaluated under noiseless simulation conditions, with performance compared against a classical Support Vector Machine baseline using identical preprocessing and evaluation metrics. While classical models achieve superior computational efficiency and higher predictive performance, QSVMs exhibit non-trivial classification behaviour on small, structured datasets when appropriate preprocessing and feature-to-qubit alignment are applied. Rather than claiming quantum advantage, this work provides an empirical assessment of the current capabilities and limitations of QML in applied geospatial analysis, positioning quantum kernel methods as complementary tools for exploratory environmental modelling under present technological constraints.
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This paper evaluates the performance of two quantum machine learning algorithms: Quantum Support Vector Machine (QSVM) and Quantum Principal Component Analysis (Q-PCA). The scalability of QSVM is analyzed across increasing dataset sizes and feature dimensions, revealing faster-than-linear growth in kernel evaluation and SVM training times. However, this computational overhead is accompanied by a notable decline in classification accuracy, highlighting challenges in applying QSVM to larger or more complex datasets. In contrast, Q-PCA demonstrates near-linear scalability with dataset size and qubit count, leveraging the independence of quantum circuit execution and structured operations to maintain efficiency. A QNN-based implementation of Q-PCA further validates its ability to align data with principal components, maximize variance, and enable effective dimensionality reduction. These findings emphasize the potential of Q-PCA for efficient quantum-enhanced data analysis while underscoring the need for optimization in QSVM and further exploration of Q-PCA’s capabilities for real-world quantum computing applications.
Global Positioning System (GPS) plays a critical role in navigation by utilizing satellite signals, but its accuracy in urban environments is often compromised by signal obstructions. Previous research has categorized GPS reception conditions into line-of-sight (LOS), non-line-of-sight (NLOS), and LOS+NLOS scenarios to enhance accuracy. This paper introduces a novel approach using quantum support vector machines (QSVM) with a ZZ feature map and fidelity quantum kernel to classify urban GPS signal reception conditions, comparing its performance against classical SVM methods. While classical SVM has been previously explored for this purpose, our study is the first to apply QSVM to this classification task. We conducted experiments using datasets from two distinct urban locations to train and evaluate SVM and QSVM models. Our results demonstrate that QSVM achieves superior classification accuracy compared to classical SVM for urban GPS signal datasets. Additionally, we emphasize the importance of appropriately scaling raw data when utilizing QSVM.
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Quantum machine learning has emerged as a promising frontier, leveraging the principles of quantum computing to enhance classical algorithms. This paper proposes an optimized Quantum Support Vector Classifier (QSVC) for the classification of the MNIST dataset (a benchmark in handwritten digit recognition). We employ a Chebyshev Parameterized Quantum Circuit (PQC) with an increased number of qubits and layers, and utilize an existing projected quantum kernel method to effectively capture the complex patterns in the data. To enhance model robustness, data augmentation techniques are applied, and dimensionality reduction is performed using Principal Component Analysis (PCA). The proposed QSVC demonstrates competitive accuracy on both the training and testing datasets, highlighting the potential of quantum kernel methods in practical machine learning applications. A comparative analysis with classical Support Vector Machines (SVM) demonstrates the feasibility of quantum methods for high-dimensional classification tasks. Additionally, we investigate the impact of varying the number of qubits in the quantum circuit, providing in-depth insights into the scalability and performance trade-offs of quantum kernel methods.