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Quantum Machine Learning in Botany: Demonstrating Clear Quantum Advantage in Multi-Species Leaf Classification Using Quantum Enhanced Random Forests Classifier

Safaa Taher Abdelhameed, Nuha Omran Abokhdair

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.2139/ssrn.6175926

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This research presents a comparative evaluation of classical and quantum machine learning models applied to the Bridge dataset. Classical algorithms like Support Vector Machine, Random Forest, and Neural Networks are benchmarked against Quantum SVM, Quantum Random Forest, and Quantum Neural Networks using identical preprocessing and training conditions. Results indicate a consistent quantum advantage, with quantum models achieving higher accuracy, stronger nonlinear feature separation, and improved minority-class detection. QSVM and QNN exhibit the most significant performance gains. Although quantum models require greater computational resources, the findings underscore the emerging effectiveness of quantum-enhanced learning for structural classification tasks in the NISQ era.
As criminal tactics become increasingly sophisticated, using advanced tools and technologies, the demand for more powerful analytical systems has surged. Although classical machine learning approaches have been widely used in criminal analysis, the exponential growth of data, driven largely by large language models (LLMs) and AI agents, has exposed their limitations. Quantum computing, rooted in the principles of quantum mechanics, presents a promising alternative that harnesses the behavior of atoms and electrons to perform complex computations. This study evaluates classical and quantum machine learning approaches for crime prediction, analyzing their efficacy in classifying criminal incidents. Classical models, such as logistic regression, XGBoost, random forest, and support vector machine, demonstrated robust performance, with ensemble methods such as random forest and XGBoost achieving particularly high effectiveness in classification tasks. In contrast, quantum models, including the variational quantum classifier, the quantum neural network, and the quantum support vector machine, showed promising theoretical advantages but lagged in practical performance due to current hardware. The findings highlight the need for further refinement of quantum techniques to bridge this gap and fully harness their potential for complex pattern recognition in criminal data analytics.
Diabetes remains one of the common chronic diseases around the world and finding a good early prediction from clinical and lifestyle data is very important for timely help. Classical machine learning models such as regression, decision trees and random forests have been used for this purpose but the complicated non‑linear connections among clinical features might need richer ways of representation. Quantum Machine Learning (QML) gives an approach using quantum feature encoding and variational circuits to find patterns that are expensive to express with classical methods. In this study we show a diabetes prediction pipeline that compares baseline models—logistic regression, decision tree and random forest—with two quantum classifiers built in Qiskit: a Quantum Support Vector Classifier (QSVC) and a Variational Quantum Classifier (VQC). Using the Pima Indians Diabetes dataset we test all models on accuracy, precision, recall and F1‑score. The results show that the classical Random Forest classifier reaches the accuracy (0.73) beating both quantum classifiers. The Quantum Support Vector Classifier (QSVC 0.66) and the Variational Quantum Classifier (VQC 0.65) are still competitive. Do not outdo the classical baselines. These results agree with reports that in the present Noisy Intermediate‑Scale Quantum (NISQ) era quantum models usually match rather than surpass well‑tuned classical approaches on standard health‑care data and that a real quantum advantage will need larger data, deeper circuits and testing, on real quantum hardware.