Classical machine learning methods - including CNNs, SVMs, PCA, Logistic Regression, and Random Forests - have achieved strong performance across fields such as computer vision, malware detection, and drug discovery. However, these models face scalability limits when trained on large or high-dimensional datasets. Quantum computing introduces superposition, interference, and entanglement, enabling quantum kernels, quantum feature maps, and hybrid quantum-classical architectures that may reduce computational cost or enhance data representation. This project implements classical versions of these algorithms alongside their quantum counterparts to evaluate differences in accuracy, efficiency, and resource demands. By comparing performance across diverse scientific and engineering datasets, the study assesses whether quantum-enhanced methods can match or surpass classical baselines under practical constraints.
Classical machine learning methods - including CNNs, SVMs, PCA, Logistic Regression, and Random Forests - have achieved strong performance across fields such as computer vision, malware detection, and drug discovery. However, these models face scalability limits when trained on large or high-dimensional datasets. Quantum computing introduces superposition, interference, and entanglement, enabling quantum kernels, quantum feature maps, and hybrid quantum-classical architectures that may reduce computational cost or enhance data representation. This project implements classical versions of these algorithms alongside their quantum counterparts to evaluate differences in accuracy, efficiency, and resource demands. By comparing performance across diverse scientific and engineering datasets, the study assesses whether quantum-enhanced methods can match or surpass classical baselines under practical 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.
This study explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms; to assess performance enhancements and computational implications across a spectrum of models. We explored various classical-to-quantum mapping methods; ranging from basis encoding and angle encoding to amplitude encoding; for encoding classical data. We conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead. This study underscores the potential of quantum data embedding to enhance classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research may involve refining quantum encoding processes to optimize computational efficiency and explore scalability for real-world applications. Our work contributes to the growing body of knowledge on the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.
Quantum-enhanced machine learning is a rapidly evolving field that aims to leverage the unique properties of quantum mechanics to enhance classical machine learning. However, the practical applicability of these methods remains an open question, particularly beyond the context of specifically crafted toy problems, and given the current limitations of quantum hardware. This study focuses on quantum kernel methods in the context of classification tasks. In particular, it examines the performance of quantum kernel estimation and quantum kernel training (QKT) in connection with two quantum feature mappings, namely, ZZFeatureMap and CovariantFeatureMap. Remarkably, these feature maps have been proposed in the literature under the conjecture of possible near-term quantum advantage and have shown promising performance in ad hoc datasets. This study aims to evaluate their versatility and generalization capabilities in a more general benchmark, encompassing both artificial and established reference datasets. Classical machine learning methods, specifically support vector machines and logistic regression, are also incorporated as baseline comparisons. Experimental results indicate that quantum methods exhibit varying performance across different datasets. Despite outperforming classical methods in ad hoc datasets, mixed results are obtained for the general case among standard classical benchmarks. The experimental data call into question a general added value of applying QKT optimization, for which the additional computational cost does not necessarily translate into improved classification performance. Instead, it is suggested that a careful choice of the quantum feature map in connection with proper hyperparameterization may prove more effective.
This thesis explores the integration of quantum computing into Machine Learning (ML), aiming to evaluate the effectiveness and limitations of quantum-enhanced models compared to classical approaches. As quantum technologies continue to develop, they offer the potential for solving complex computational problems more efficiently, particularly in fields requiring high-dimensional data processing. A literature review is conducted to contextualize current advancements in Quantum Machine Learning, highlighting theoretical benefits and existing practical limitations. This work investigates whether quantum methods, specifically the Quantum Support Vector Classifier (QSVC), can provide measurable improvements over its classical counterpart. The QSVC, implemented using IBM’s Qiskit ML library, replaces traditional kernel functions with quantum kernels computed via quantum circuits and feature maps, embedding classical data into a Hilbert space. The experimental portion of this thesis compares classical and quantum models on two distinct datasets: one from the healthcare domain and another from particle physics. Various parameters are tested in a constrained grid search due to the high computational demands of quantum circuits. Results indicate that while quantum models are significantly more resource-intensive, they can achieve comparable or even improved performance on data that has more complex relations between their variables. However, scalability remains a major challenge. This work concludes that although QSVC does not yet consistently outperform classical models, it offers advantages in specific contexts and lays the groundwork for identifying future use cases where quantum computing may offer a practical advantage in ML.
Classical machine learning has practical significant advancements and extensive acceptance across various domains, enabling the growth of precise predictive models. The purpose of this work is to examine the accuracy gained by various machine learning algorithms, both classical and quantum. In addition, the study delves into the fundamental ideas and principles that form the backbone of quantum machine learning algorithms, such as quantum data representation, quantum feature mapping, and quantum optimization strategies. Experiments use classical machine learning models trained and tested on traditional hardware to measure evaluation metrics like classification accuracy, precision, recall, and F1-score for quantum machine learning models executed on quantum computing platforms or simulators. In quantum SVM, the seed dataset obtained 100% accuracy, and the supermarket dataset obtained 69% accuracy when compared to classical SVM. The findings imply that, under some conditions, quantum machine learning algorithms show the capacity to beat their conventional counterparts in terms of accuracy.
Background: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed predictive analytics across domains such as healthcare, finance, and environmental sciences. However, traditional ML models face limitations when dealing with large-scale, high-dimensional datasets, particularly due to computational inefficiencies and the "curse of dimensionality." Quantum computing offers promising solutions by leveraging superposition and entanglement to perform complex computations at exponential speeds. Objective: This study aims to explore how quantum computing can enhance AI and ML models, particularly in the domain of predictive analytics. It proposes a comprehensive Quantum-AI framework that integrates quantum technologies into existing predictive systems to overcome the challenges posed by classical approaches. Methods: The study employed hybrid quantum-classical models using frameworks like Qiskit, TensorFlow Quantum, and Pennylane. Datasets were sourced from real-world applications in finance, healthcare, and climate science. Experiments were designed to compare classical and quantum- enhanced models on metrics such as accuracy, scalability, and computational time. Specific algorithms used include Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN). Result: The proposed quantum-enhanced models showed significant improvements: • Accuracy increased from 85% (classical) to 92% (quantum) in finance-related predictions. • Diagnostic models in healthcare achieved a 94% accuracy rate and reduced training time by 50%. • Climate modeling tasks achieved 90% improved accuracy and 20% faster simulation times. The results validate the effectiveness of quantum models in processing large, complex datasets more efficiently. Conclusion: Quantum-enhanced AI presents a transformative approach to predictive analytics by addressing the core limitations of classical ML systems. This study demonstrates the practical feasibility and industrial relevance of integrating quantum computing into AI workflows. It opens avenues for further research in developing scalable, domain-specific quantum algorithms and hybrid AI models for real-world applications.