The Rio Santiago basin is one of the most environmentally stressed river systems in Mexico, with persistent organic, nutrient, microbial, surfactant, and metal contamination. This study develops a near-term quantum machine learning workflow for environmental monitoring and water-pollution profiling using multivariate records from 13 stations between 2009 and 2022. QML is evaluated here because quantum feature maps can define nonlinear, interaction-rich kernels that remain executable on present quantum hardware, providing an alternative representation to compare with classical PCA, RBF, UMAP, and HDBSCAN baselines rather than a presumed computational advantage. After quality screening, log transformation, standardization, and domain-guided feature selection, pollution profiles are evaluated across PCA, RBF spectral clustering, UMAP/KMeans, UMAP/HDBSCAN, a simulated ZZ-style quantum feature-map kernel, and Qiskit Runtime hardware evaluations of the same kernel concept. The initial cleaned-data results show that classical PCA clustering identifies broad lower-load, high organic/surfactant, and rain-season solids/microbial profiles. UMAP/HDBSCAN provides the strongest cleaned full-sample nonlinear baseline, with a silhouette score of 0.568 after excluding 177 noise samples. The simulated quantum-kernel representation separates station-linked gradients, while matched n = 650 stability diagnostics show near-identical quantum-kernel clustering across random initializations (mean ARI = 0.994 for cleaned data) but retain the RBF kernel as the strongest nonlinear comparator. Two 24-sample Qiskit hardware runs and two matched 8-record hardware checks provide proof-of-execution evidence. The analysis is framed as a controlled representation study, not as a claim of quantum advantage.
Geochemical data analysis presents challenges due to multi-element interactions and non-linear spatial patterns that classical methods struggle to capture. This study compares quantum machine learning (QML) clustering methods applied to geochemical data from the Antofagasta region of Chile, introducing a methodological framework using database independence constraints to validate that identified patterns reflect geochemical variation rather than sampling artifacts. Preliminary comparison of four quantum approaches—the Quantum Approximate Optimization Algorithm (QAOA), amplitude embedding, angle embedding, and the Quantum Boltzmann Machine (QBM)—identified QBM as achieving the best clustering quality with superior computational efficiency. We then systematically explored 150 QBM configurations varying qubit count (2–6), feature aggregation, principal component analysis (PCA) preprocessing, and training steps, evaluating clustering performance on 643 multi-element samples. A two-stage selection framework combining domain constraints (
K≥4 ) with Jensen-Shannon Divergence filtering (max JSD < 0.20) ensures identified patterns reflect geochemical variation rather than database artifacts. To ensure a fair comparison, the best QBM configuration was benchmarked against a broad, comparably tuned panel of classical clustering methods (1,900 configurations spanning
K -means, Gaussian mixtures, agglomerative, spectral, and density-based clustering over linear, kernel, and manifold representations) and against the other three quantum methods, all evaluated under the identical protocol. Scored on common geochemical ground, the best QBM configuration attained a silhouette of 0.476 versus 0.262 for the strongest classical method (0.722 versus 0.539 in each method’s own feature space); the advantage was positive across all bootstrap resamples (95% confidence interval
[+0.145,+0.275] ) and persisted under a compositional (centered-log-ratio) transform, while a subsample-refit estimate places the de-biased QBM silhouette at
0.42±0.08 . The JSD filter rejected 49% of QBM configurations that would have produced database-dominated clusters, demonstrating the importance of this validation step. The optimal four-cluster solution (
K=4 ) identified interpretable geochemical populations: a regional background (88.5%) and three small anomaly clusters—arsenic-enriched (7.0%), boron-rich (1.2%), and a multi-metal Cu–Pb–Zn cluster reaching 8,131 mg/kg Cu (3.3%)—whose signatures are consistent with a mixture of natural mineralization/alteration and anthropogenic, mining-related contamination. Cross-database validation confirmed statistically significant convergence (Jaccard similarity = 0.702, random subset permutation
p=0.028 ; Spearman
ρ=0.566 ), with seven core signature elements (B, Cd, Co, Cr, Fe, Mn, Ni) converging across all three independent surveys. These results indicate that, for this multi-source geochemical dataset and under database independence constraints, QBM yields better-separated, anomaly-sensitive clusters than a broad classical baseline and the other quantum methods tested, with the advantage arising from its isolation of small, geochemically coherent anomaly populations; given the limited qubit counts and the small, noiseless simulations used here, this should not be read as evidence of a general quantum advantage. We provide guidelines for applying QBM to multi-source geoscientific datasets. INDEX TERMS Quantum machine learning, geochemical clustering, pattern recognition, unsupervised learning, quantum Boltzmann machines, Antofagasta region.
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.
Quantum machine learning (QML) has emerged as a promising method for overcoming the computational limitations of classical machine learning when analyzing large and complex data sets. This project investigates the application of QML algorithms to real-world science and engineering problems, with a focus on civil and environmental engineering datasets. We develop and evaluate a Python-based system, implemented in Google Colab, that integrates multiple quantum computing frameworks, including PennyLane, TensorFlow Quantum, and Qiskit, to implement and compare several QML models against their classical counterparts. The proposed system explores a range of algorithms such as Quantum Neural Networks, Quantum Support Vector Machines, Quantum Principal Component Analysis, and Quantum Logistic Regression across at least five engineering use cases, including structural health monitoring, traffic flow prediction, air quality, flood forecasting, and pollution modeling. Performance is evaluated using accuracy, computational efficiency, and scalability. The results highlight scenarios in which QML demonstrates a potential advantage over a classical approach, while also identifying the current limitations that come with near-term quantum devices. This work contributes to a modular framework for learning and applying quantum machine learning and provides insight into its practical viability and application within science and engineering applications.
Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous benchmarks against strong classical baselines remain scarce. We present a comprehensive empirical study of quantum kernel support vector machines (QSVMs) across nine binary classification datasets, four quantum feature maps, three classical kernels, and multiple noise models, totalling 970 experiments with strict nested cross-validation.
Our analysis spans four phases: (i) statistical significance testing, revealing that none of 29 pairwise quantum-classical comparisons reach significance at
α=0.05; (ii) learning curve analysis over six training fractions, showing steeper quantum slopes on six of eight datasets that nonetheless fail to close the gap to the best classical baseline; (iii) hardware validation on IBM ibm_fez (Heron r2), demonstrating kernel fidelity
r≥0.976 across six experiments; and (iv) seed sensitivity analysis confirming reproducibility (mean CV 1.4%).
A Kruskal-Wallis factorial analysis reveals that dataset choice dominates performance variance (
ε2=0.73), while kernel type accounts for only 9%. Spectral analysis offers a mechanistic explanation: current quantum feature maps produce eigenspectra that are either too flat or too concentrated, missing the intermediate profile of the best classical kernel, the radial basis function (RBF). Quantum kernel training (QKT) via kernel-target alignment yields the single competitive result -- balanced accuracy 0.968 on breast cancer -- but with ~2,000x computational overhead.
Our findings provide actionable guidelines for quantum kernel research. The complete benchmark suite is publicly available to facilitate reproduction and extension.
Reproducibility archive (code + manuscript) for the paper "Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation" (arXiv:2604.18837). The archive contains the full Python benchmark framework, experiment configurations, unit tests, and the compiled manuscript. Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous benchmarks against strong classical baselines remain scarce. We present a comprehensive empirical study of quantum kernel support vector machines (QSVMs) across nine binary classification datasets, four quantum feature maps, three classical kernels, and multiple noise models, totalling 970 experiments with strict nested cross-validation. Our analysis spans four phases: (i) statistical significance testing; (ii) learning curve analysis; (iii) hardware validation on IBM ibm_fez (Heron r2); and (iv) seed sensitivity analysis. A Kruskal–Wallis factorial analysis reveals that dataset choice dominates performance variance (
ε2=0.73), while kernel type accounts for only 9%. Spectral analysis offers a mechanistic explanation: current quantum feature maps produce eigenspectra that are either too flat or too concentrated, missing the intermediate profile of the best classical kernel (RBF). The complete benchmark suite is publicly available to facilitate reproduction and extension.
An interesting approach to using noisy intermediate-scale quantum (NISQ) devices is hybrid classical-quantum machine learning (QML). In these methods, classical processors handle optimisation and large-scale computation, while quantum hardware is devoted to tasks like feature mapping, nonlinear transformations, or kernel evaluation. Practical near-term demonstrations are made possible by this division of labour, which also mitigates existing hardware restrictions. In specialised fields like molecular modelling, materials discovery, and small-sample learning issues, recent advancements in variational quantum circuits, hybrid neural networks, and quantum kernel techniques have produced promising outcomes. However, scalability and wider applicability are still hampered by enduring issues including noise, barren plateaus, and the expense of repeated measurements. Long-term developments will require fault tolerance, logical qubits, and established software infrastructures, whereas near-term success depends on noise-aware algorithm design, repeatable experimental benchmarks, and enhanced error-mitigation strategies. When taken as a whole, these advancements show a viable path to achieving quantum advantage in machine learning. Quanta 2026; 15: 1–12.