Abstract Quantum machine learning (QML) leverages the potential of machine learning (ML) to explore the subtle patterns in huge datasets of complex nature with quantum advantages. QML accelerates materials research with active screening of chemical space, identifying novel materials for practical applications, and classifying structurally diverse materials given their measured properties. This study analyzes the performance of three efficient quantum machine learning algorithms viz., variational quantum classifier (VQC), quantum support vector classifier (QSVC), and quantum neural networks (QNN) for distinguishing transition metal chalcogenides (TMCs) from transitional metal oxides (TMOs). By employing feature selection, classical machine learning achieves 100% accuracy whereas QML achieves the highest performance of 99% and 98% for test and train data respectively on QSVC. Further, to extend the QML models for structural and functional analysis of materials that cannot be inferred directly from the formula, stability analysis, and magnetic nature analysis on 1000 and 500 materials are performed, respectively. The stability analysis achieves 78% accuracy with QSVC and the magnetic nature analysis achieves 88% with QNN establishing the competence of QML models. This study proves that QML models are remarkable in materials classification and analysis which fuels the task of materials discovery in the future.
Abstract Quantum machine learning (QML) leverages the potential of machine learning (ML) to explore the subtle patterns in huge datasets of complex nature with quantum advantages. QML accelerates materials research with active screening of chemical space, identifying novel materials for practical applications, and classifying structurally diverse materials given their measured properties. This study analyzes the performance of three efficient quantum machine learning algorithms viz., variational quantum classifier (VQC), quantum support vector classifier (QSVC), and quantum neural networks (QNN) for distinguishing transition metal chalcogenides (TMCs) from transitional metal oxides (TMOs). By employing feature selection, classical machine learning achieves 100% accuracy whereas QML achieves the highest performance of 99% and 98% for test and train data respectively on QSVC. Further, to extend the QML models for structural and functional analysis of materials that cannot be inferred directly from the formula, stability analysis, and magnetic nature analysis on 1000 and 500 materials are performed, respectively. The stability analysis achieves 78% accuracy with QSVC and the magnetic nature analysis achieves 88% with QNN establishing the competence of QML models. This study proves that QML models are remarkable in materials classification and analysis which fuels the task of materials discovery in the future.
Quantum machine learning (QML) leverages the potential from machine learning to explore the subtle patterns in huge datasets of complex nature with quantum advantages. This exponentially reduces the time and resources necessary for computations. QML accelerates materials research with active screening of chemical space, identifying novel materials for practical applications and classifying structurally diverse materials given their measured properties. This study analyzes the performance of three efficient quantum machine learning algorithms viz., variational quantum eigen solver (VQE), quantum support vector machine (QSVM) and quantum neural networks (QNN) for the classification of transition metal chalcogenides and oxides (TMCs &TMOs). The analysis is performed on three datasets of different sizes containing 102, 192 and 350 materials with TMCs and TMOs labelled as +1 and -1 respectively. By employing feature selection, classical machine learning achieves 100% accuracy whereas QML achieves the highest performance of 99% and 98% for test and train data respectively on QSVC. This study establishes the competence of QML models in materials classification and explores the quantum circuits in terms of over-fitting using the circuit descriptors expressibility and entangling capability. In addition, the perspectives on QML in materials research with noisy intermediate scale quantum (NISQ) devices is given.
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.
Quantum machine learning (QML), ML on quantum computers, offers a promising approach for discovering and screening novel materials. This study introduces a hybrid classical-quantum ML method using a variational quantum classifier to identify simple perovskite structures within a data set of ABO 3 compounds. The model is trained using a data set of 397 known ABO 3 compounds, with 254 perovskites and 143 non-perovskite structures labeled as +1 and −1, respectively. By considering feature correlation and eliminating less important features, the QML system achieves an optimal accuracy of 88% for training data and 87% for unseen test data. These results demonstrate the potential of QML in materials science classification tasks, even with limited training data, leveraging the intrinsic properties of quantum computation to enhance the investigation of materials. In addition, perspectives on QML applications in materials science are discussed.
This study explores the performance of Quantum Support Vector Classifiers (QSVCs) and Quantum Neural Networks (QNNs) in comparison to classical models for machine learning tasks. By evaluating these models on the Iris and MNIST-PCA datasets, we find that quantum models tend to outperform classical approaches as the problem complexity increases. While QSVCs generally provide more consistent results, QNNs exhibit superior performance in higher-complexity tasks due to their increased quantum load. Additionally, we analyze the impact of hyperparameter tuning, showing that feature maps and ansatz configurations significantly influence model accuracy. We also compare the PennyLane and Qiskit frameworks, concluding that Qiskit provides better optimization and efficiency for our implementation. These findings highlight the potential of Quantum Machine Learning (QML) for complex classification problems and provide insights into model selection and optimization strategies
Quantum Machine Learning (QML) has emerged as a promising paradigm for addressing increasing computational and representational demands in materials informatics. While classical models such as Support Vector Machines (SVM) achieve strong predictive performance, they often struggle to capture complex, highly correlated interactions in high-dimensional materials data. QML addresses this challenge by leveraging quantum-mechanical principles to construct expressive feature embeddings, where prospective quantum advantage lies in generating feature spaces that are difficult to approximate classically. In this study, 1,000 crystalline compounds from the Open Quantum Materials Database (OQMD) are evaluated in a binary classification task based on formation-energy stability. The dataset is normalized, reduced to four dimensions via Principal Component Analysis (PCA), and encoded into quantum circuits. Three QML models—QSVM, VQC, and QNN—are benchmarked against a classical SVM using repeated stratified evaluation. Results show that the classical SVM achieves the highest accuracy (91.8% ± 0.012), followed by QSVM (60.8% ± 0.035), while VQC and QNN perform significantly worse. This gap is driven by limited qubit capacity, encoding inefficiencies, restricted circuit expressivity, and optimization challenges. Nevertheless, QSVM demonstrates stable performance, suggesting that potential quantum advantage may emerge from improved feature encoding and kernel design rather than deeper variational circuits.
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.