This research provides a rigorous empirical benchmark comparing classical and quantum machine learning algorithms across PCA, Random Forest, Neural Networks, and CNNs. We evaluate performance on four datasets, revealing quantum advantages only in specific scenarios: quantum neural networks achieved 34-46% better precision/recall on imbalanced classification, while classical methods dominated in efficiency and scalability. Critical findings include quantum algorithms requiring 57,500x more training time and becoming intractable beyond 8-10 qubits. Our implementation methodology and performance baselines provide practitioners with concrete guidance for algorithm selection in the current NISQ era.
This research provides a rigorous empirical benchmark comparing classical and quantum machine learning algorithms across PCA, Random Forest, Neural Networks, and CNNs. We evaluate performance on four datasets, revealing quantum advantages only in specific scenarios: quantum neural networks achieved 34-46% better precision/recall on imbalanced classification, while classical methods dominated in efficiency and scalability. Critical findings include quantum algorithms requiring 57,500x more training time and becoming intractable beyond 8-10 qubits. Our implementation methodology and performance baselines provide practitioners with concrete guidance for algorithm selection in the current NISQ era.
Quantum Machine Learning (QML) has emerged as an interdisciplinary field that harnesses the potential of quantum computing to revolutionize classical machine learning paradigms. This paper presents a benchmarking study focused on four quantum classifier algorithms: Quantum Support Vector Classifier (QSVC), Variational Quantum Circuit (VQC), Quantum Neural Network Classifier (QNNC) and Pegasos Quantum Support Vector Classifier (PegasosQSVC). These quantum classifiers hold promise for unlocking quantum advantages in classification tasks. However, a systematic assessment of their performance across diverse datasets is yet to be fully explored. In this research study, we evaluate and compare the classification performance of quantum machine learning classifiers on various datasets (Breast Cancer, Diabetes, and Titanic) and compare them to a classical model. Our findings revealed that PegasosQSVC consistently maintained high performance across all datasets with the highest average accuracy, recall, and F1-score values of 0.81, 0.72, and 0.71, respectively, followed by VQC, followed by VQC, while QSVC and QNNC perform variably. Nevertheless, QSVC was found to balance performance and training time, achieving an average accuracy of 0.81 and a training time of 177 seconds. Overall, the classical model surpassed QML models as it was up to 10% higher in average accuracy.
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.
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.
In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and conducted the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing.
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.
Quantum machine learning (QML) represents a convergence of quantum computing and statistical learning theory with transformative potential for high-throughput computational materials discovery. This empirical study systematically benchmarks six QML architectures — quantum kernel support vector machines, variational quantum eigensolvers augmented by graph neural networks, quantum approximate optimization algorithm hybrids, quantum random forests, quantum Boltzmann machines, and parameterized quantum circuit-based neural networks — against their classical analogues across five material classes comprising perovskite oxides, battery cathode materials, two-dimensional semiconductor alloys, metal-organic frameworks, and high-entropy alloys. Using 14,720 DFT-computed property records drawn from the Materials Project and AFLOW databases as the common evaluation corpus, the QML methods achieved mean absolute error reductions of 38–61% and training time compressions of 29–61% relative to equivalent classical estimators at equivalent dataset sizes. The quantum random forest architecture exhibited the strongest overall performance (MAE = 0.026 eV; R² = 0.978), while hardware-native implementations on IBM Quantum Eagle and IonQ Forte achieved practical speed-ups of 28x–41x over CPU-based baselines. Barren plateau pathology and noise-induced gradient suppression remained the principal barriers to scaling beyond 50 qubits on current NISQ hardware. The empirical results of this study established principal conclusions that advance the theoretical and practical understanding of QML in materials discovery contexts. These findings provide grounding for deploying QML in production materials informatics pipelines.