This study presents a comprehensive comparison of multiple optimization algorithms applied to a quantum classification model, utilizing the Cleveland dataset. Specifically, the research focuses on three prominent optimizers—COBYLA, L-BFGS-B, and ADAM—each employing distinct methodologies and widely recognized in the domain of quantum machine learning. The performance of predictive models using these optimizers is rigorously evaluated through key metrics, including accuracy, precision, recall, and F1 score. The findings reveal that the COBYLA optimizer outperforms the L-BFGS-B and ADAM optimizers across all performance metrics, achieving an accuracy of 92%, precision of 89%, recall of 97%, and F1 score of 93%. Furthermore, the COBYLA optimizer exhibits superior computational efficiency, requiring only 1 min of training time compared to 6 min for L-BFGS-B and 10 min for ADAM. These results underscore the critical role played by optimizer selection in enhancing model performance and efficiency in quantum machine learning applications, offering valuable insights for practitioners in the field.
This study presents a comprehensive comparison of multiple optimization algorithms applied to a quantum classification model, utilizing the Cleveland dataset. Specifically, the research focuses on three prominent optimizers—COBYLA, L-BFGS-B, and ADAM—each employing distinct methodologies and widely recognized in the domain of quantum machine learning. The performance of predictive models using these optimizers is rigorously evaluated through key metrics, including accuracy, precision, recall, and F1 score. The findings reveal that the COBYLA optimizer outperforms the L-BFGS-B and ADAM optimizers across all performance metrics, achieving an accuracy of 92%, precision of 89%, recall of 97%, and F1 score of 93%. Furthermore, the COBYLA optimizer exhibits superior computational efficiency, requiring only 1 min of training time compared to 6 min for L-BFGS-B and 10 min for ADAM. These results underscore the critical role played by optimizer selection in enhancing model performance and efficiency in quantum machine learning applications, offering valuable insights for practitioners in the field.
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.
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 integrates quantum computing with classical machine learning techniques to enhance computational power and efficiency. A major challenge in quantum machine learning is developing robust quantum classifiers capable of accurately processing and classifying complex datasets. In this work, we present an advanced approach leveraging data re-uploading, a strategy that cyclically encodes classical data into quantum states to improve classifier performance. We examine two cost functions, fidelity and trace distance, across various quantum classifier configurations, including single-qubit, two-qubit, and entangled two-qubit systems. Additionally, we evaluate four optimization techniques (L-BFGS-B, COBYLA, Nelder-Mead, and SLSQP) to determine their effectiveness in optimizing quantum circuits for both linear and non-linear classification tasks. Our results show that the choice of optimization method significantly impacts classifier performance, with L-BFGS-B and COBYLA often yielding superior accuracy. The two-qubit entangled classifier shows improved accuracy over its non-entangled counterpart, albeit with increased computational cost. Also, the two-qubit entangled classifier is the best option for real-world random datasets in terms of accuracy and computational cost. Linear classification tasks generally exhibit more stable performance across optimization techniques compared to non-linear tasks. Our findings highlight the potential of data re-uploading in quantum machine learning, outperforming existing quantum classifier models in terms of accuracy and robustness. This work contributes to the growing field of quantum machine learning by providing a comprehensive comparison of classification strategies and optimization techniques in quantum computing environments, offering a foundation for developing more efficient and accurate quantum classifiers.
Quantum Machine Learning is gaining traction by leveraging quantum advantage to outperform classical Machine Learning. Many classical and quantum optimizers have been proposed to train Parameterized Quantum Circuits in the simulation environment, achieving high accuracy and fast convergence speed. However, to the best of our knowledge, currently there is no related work investigating these optimizers on multiple algorithms, which may lead to the selection of suboptimal optimizers. In this paper, we first benchmark the most popular classical and quantum optimizers, such as Gradient Descent (GD), Adaptive Moment Estimation (Adam), and Quantum Natural Gradient Descent (QNG), through the Quantum Compilation algorithm. Evaluated metrics include the lowest cost value and the wall time. The results indicate that Adam outperforms other optimizers in terms of convergence speed, cost value, and stability. Furthermore, we conduct additional experiments on multiple algorithms with Adam variants, demonstrating that the choice of hyperparameters significantly impacts the optimizer's performance.
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.
Abstract Quantum machine learning integrates quantum computing with classical machine learning techniques to enhance computational power and efficiency. A major challenge in Quantum machine learning is developing robust quantum classifiers capable of accurately processing and classifying complex datasets. In this work, we present an advanced approach leveraging data re-uploading, a strategy that cyclically encodes classical data into quantum states to improve classifier performance. We examine two cost functions—fidelity and trace distance—across various quantum classifier configurations, including single-qubit, two-qubit, and entangled two-qubit systems. Additionally, we evaluate four optimization techniques (L-BFGS-B, COBYLA, Nelder-Mead, and SLSQP) to determine their effectiveness in optimizing quantum circuits for both linear and non-linear classification tasks. Our results show that the choice of optimization method significantly impacts classifier performance, with L-BFGS-B and COBYLA often yielding superior accuracy. The two-qubit entangled classifier shows improved accuracy over its non-entangled counterpart, albeit with increased computational cost. Also, the two-qubit entangled classifier are the best option for real word random dataset in order to accuracy and computational cost. Linear classification tasks generally exhibit more stable performance across optimization techniques compared to non-linear tasks. Our findings highlight the potential of data re-uploading in Quantum machine learning outperforming existing quantum classifier models in terms of accuracy and robustness. This work contributes to the growing field of Quantum machine learning by providing a comprehensive comparison of classification strategies and optimization techniques in quantum computing environments, offering a foundation for developing more efficient and accurate quantum classifiers.