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.
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.
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) is an emerging interdisciplinary field that combines the principles of quantum mechanics and machine learning to develop algorithms that can potentially outperform classical algorithms in certain tasks. QML leverages the unique properties of quantum systems, such as superposition and entanglement, to process information in ways that are not possible with classical computers. This paper provides a comprehensive overview of QML, including its principles, algorithms, and applications. We focus particularly on supervised learning methods, which involve training a quantum model on labeled data to make predictions on new, unseen data. We discuss the potential of QML to revolutionize various domains, such as finance, chemistry, and materials science, and highlight the challenges associated with the development and implementation of QML algorithms, including the need for more advanced quantum hardware and software. This paper aims to provide a clear understanding of the current state of QML research and its potential impact on future computational capabilities.
Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics and machine learning to develop algorithms that can potentially outperform classical algorithms in certain tasks. QML leverages the unique properties of quantum systems, such as superposition and entanglement, to process information in ways that are not possible with classical computers. This paper provides a comprehensive overview of QML, including its principles, algorithms, and applications. We focus particularly on supervised learning methods, which involve training a quantum model on labeled data to make predictions on new, unseen data. We discuss the potential of QML to revolutionize various domains, such as finance, chemistry, and materials science, and highlight the challenges associated with the development and implementation of QML algorithms, including the need for more advanced quantum hardware and software. This paper aims to provide a clear understanding of the current state of QML research and its potential impact on future computational capabilities.
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.
The meeting of artificial intelligence (AI) and quantum computing is already a reality; quantum machine learning (QML) promises the design of better regression models. In this work, we extend our previous studies of materials discovery using classical active learning (AL), which showed remarkable economy of data, to explore the use of quantum algorithms within the AL framework (QAL) as implemented in the MLChem4D and QMLMaterials codes. The proposed QAL uses quantum support vector regressor (QSVR) or a quantum Gaussian process regressor (QGPR) with various quantum kernels and different feature maps. Data sets include perovskite properties (piezoelectric coefficient, band gap, energy storage) and the structure optimization of a doped nanoparticle (3Al@Si11) chosen to compare with classical AL results. Our results revealed that the QAL method improved the searches in most cases, but not all, seemingly correlated with the “roughness” of the data. QAL has the potential of finding optimum solutions, within chemical space, in materials science and elsewhere in chemistry.