Predicting the electronic bandgap ($E_{g}$) of hybrid organic-inorganic halide perovskites (HOIPs) remain a fundamental challenge in the design of high-efficiency solar cells and light-emitting diodes. While classical machine learning (ML) methods have achieved competitive accuracy, their reliance on fixed kernel spaces limits their ability to capture the intricate quantum correlations underlying$E_{g}$variations. To address this limitation, we propose a Hybrid Quantum Kernel Support Vector Regression (QKSVR) framework that integrates quantum feature encoding with classical regression. The model employs an optimized 3-qubit, single-layer quantum feature map constructed using RY rotations, T-gates for enhanced nonlinearity, and CZ entanglement for quantum correlation mapping. This design embeds compositional descriptors into a high-dimensional Hilbert space, enabling the model to learn electronic structure dependencies inaccessible to classical kernels. Feature selection was guided through the Kernel-Target Alignment (KTA) metric, a quantum-native criterion that identified the optimal descriptor subset (Cs, I,$t$) yielding a peak KTA of 0.8337. The resulting QKSVR achieved an outstanding predictive performance with$R^{2}=0.9806$and$\text{RMSE}=0.0618 \text{eV}$on the benchmark perovskite dataset (10.1007/s40033-023-00553-Z)[5]. Remarkably, this accuracy was achieved using only three chemical descriptors, underscoring the efficiency of quantum feature representations. The proposed KTA-driven hybrid modelling paradigm demonstrates the potential of quantum kernels in advancing materials informatics by revealing complex structure-property relationships beyond the reach of conventional ML models.
Predicting the electronic bandgap (Eg) of hybrid organic-inorganic halide perovskites (HOIPs) remain a fundamental challenge in the design of high-efficiency solar cells and light-emitting diodes. While classical machine learning (ML) methods have achieved competitive accuracy, their reliance on fixed kernel spaces limits their ability to capture the intricate quantum correlations underlyingEgvariations. To address this limitation, we propose a Hybrid Quantum Kernel Support Vector Regression (QKSVR) framework that integrates quantum feature encoding with classical regression. The model employs an optimized 3-qubit, single-layer quantum feature map constructed using RY rotations, T-gates for enhanced nonlinearity, and CZ entanglement for quantum correlation mapping. This design embeds compositional descriptors into a high-dimensional Hilbert space, enabling the model to learn electronic structure dependencies inaccessible to classical kernels. Feature selection was guided through the Kernel-Target Alignment (KTA) metric, a quantum-native criterion that identified the optimal descriptor subset (Cs, I,t) yielding a peak KTA of 0.8337. The resulting QKSVR achieved an outstanding predictive performance withR2=0.9806andRMSE=0.0618eVon the benchmark perovskite dataset (10.1007/s40033-023-00553-Z)[5]. Remarkably, this accuracy was achieved using only three chemical descriptors, underscoring the efficiency of quantum feature representations. The proposed KTA-driven hybrid modelling paradigm demonstrates the potential of quantum kernels in advancing materials informatics by revealing complex structure-property relationships beyond the reach of conventional ML models.
Layering two-dimensional (2D) materials into van der Waals bilayers provides an effective method to achieve innovative quantum states and tunable electronic properties. Exploring the extensive configurational space resulting from various layer combinations, twist angles, and stacking patterns presents a significant computing challenge. As the volume of accessible datasets expands, training machine learning models on conventional hardware may ultimately become excessively expensive, prompting the advancement of quantum machine learning (QML) methodologies for materials discovery. This study presents a quantum-enhanced machine learning framework for predicting the bandgaps of van der Waals bilayers utilizing quantum simulators. We start with conventional feature selection to ascertain the most important descriptors affecting electronic properties. The properties are subsequently encoded into quantum states utilizing parameterized quantum circuits. We evaluated three quantum feature maps, namely ZFeatureMap, ZZFeatureMap, and PauliFeatureMap, and observed task-dependent performance. For the classification task, ZFeatureMap with two repetitions gave the strongest quantum-classifier performance, whereas for Quantum Support Vector Regressor (QSVR) bandgap regression, PauliFeatureMap with one repetition achieved the lowest average root-mean-square error (RMSE). These results indicate that feature-map choice should be optimized separately for classification and regression rather than being interpreted as the universal superiority of a single feature map. Using these feature-map comparisons, we employ a QSVR and a variational quantum regressor to predict the bandgaps of 1850 semiconducting vdW bilayers, illustrating that QML may attain significant predictive accuracy despite limited training data. Overall, the results demonstrate the feasibility of a simulator-based QML workflow for materials-property prediction. The quantum classifiers are competitive with the classical support vector classifier (SVC) baseline for zero-gap/non-zero-gap classification, whereas the classical Support Vector Regression (SVR) achieves the lowest RMSE in the regression task. Therefore, the present study should be interpreted as an exploratory benchmark of hybrid classical-quantum models for van der Waals (vdW) bilayers rather than as evidence of a definitive quantum performance advantage.
Predicting molecular properties is a cornerstone of drug discovery, materials science, and quantum chemistry, where accuracy and speed are critical. Classical machine learning mod- els offer rapid predictions but often fail to capture the intricate quantum interactions governing molecular behavior. Our team investigated quantum kernel methods to predict properties of the QM9 dataset, a widely recognized benchmark containing134,000organic molecules with diverse chemical structures. By encoding molecular features into quantum circuits, we mapped classical data into highdimensional Hilbert spaces to enhance prediction accuracy. Our pipeline encompasses data preprocessing, quantum circuit design, model training with support vector regression, and evaluation of ten key molecular properties. The quantum kernel approach delivered promising results, achieving mean absolute errors of 0.13 Debye for dipole moment and 0.08 eV for HOMOLUMO gap, even when executed on noisy quantum devices. This work provides a comprehensive, reproducible pipeline, benchmarks quantum and classical models, and discusses practical challenges, contributing significantly to the field of quantumenhanced computational chemistry.
To accelerate the process of materials design, materials science has increasingly used data driven techniques to extract information from collected data. Specially, machine learning (ML) algorithms, which span the ML discipline, have demonstrated ability to predict various properties of materials with the level of accuracy similar to explicit calculation of quantum mechanical theories, but with significantly reduced run time and computational resources. Within ML, graph neural networks have emerged as an important algorithm within the field of machine learning, since they are capable of predicting accurately a wide range of important physical, chemical and electronic properties due to their higher learning ability based on the graph representation of material and molecular descriptors through the aggregation of information embedded within the graph. In parallel with the development of state of the art classical machine learning applications, the fusion of quantum computing and machine learning have created a new paradigm where classical machine learning model can be augmented with quantum layers which are able to encode high dimensional data more efficiently. Leveraging the structure of existing algorithms, we developed a unique and novel gradient free hybrid quantum classical convoluted graph neural network (HyQCGNN) to predict formation energies of perovskite materials. The performance of our hybrid statistical model is competitive with the results obtained purely from a classical convoluted graph neural network, and other classical machine learning algorithms, such as XGBoost. Consequently, our study suggests a new pathway to explore how quantum feature encoding and parametric quantum circuits can yield drastic improvements of complex ML algorithm like graph neural network.
Abstract Quantum and classical machine learning are fundamentally connected through kernel methods, with kernels serving as inner products of feature vectors in high-dimensional spaces, forming their foundation. Among commonly used kernels, the Gaussian kernel plays a prominent role in radial basis function neural network (RBFNN) for regression tasks. Nonetheless, the localized response property of the Gaussian kernel, which emphasizes relationships between nearby data points, limits its capacity to model interactions among more distant data points. As a result, it may potentially overlook the broader structural dependencies present within the dataset. In contrast, quantum kernels are commonly evaluated by explicitly generating quantum states and computing their inner products, thus leveraging additional quantum dimensions and capturing more intricate and complex data patterns. With the motivation of overcoming the problem above, we develop a hybrid quantum–classical model, called quantum kernel-based feedforward neural network (QKFNN) by leveraging quantum kernel methods (QKMs) to improve the prediction accuracy of RBFNN. In this study, we begin with a comprehensive introduction to QKMs, after which we present the architecture of QKFNN. To further refine model performance, an optimization strategy based on the general unitary transformation that involves a rotation factor is employed to obtain an optimized quantum kernel. The effectiveness of QKFNN is validated through experiments on synthetic and real-world datasets.
Classical machine learning models and quantum kernel methods often struggle to capture quantum-coherent molecular features under the constraints of noisy intermediate-scale quantum (NISQ) hardware, limiting both predictive accuracy and scalability. This paper introduces the Molecular Quantum Particle Algorithm (MQPA), a hybrid quantum–classical framework designed to achieve chemically accurate property prediction by integrating handcrafted molecular descriptors with parameterized quantum circuits. Molecular inputs, expressed as SMILES strings, are processed via RDKit and encoded through angle-based quantum gates with entangling layers in Qiskit [1]. Quantum parameters are optimized using simultaneous perturbation stochastic approximation (SPSA) [2], while classical regression layers leverage Adam [3] with Low-Rank Adaptation (LoRA) fine-tuning [4]. Across 38 systematically varied subsets of the QM9 benchmark [5,6], MQPA achieves a mean absolute error (MAE) of 0.03 kcal/mol, outperforming both deep learning and quantum kernel baselines by 41% and surpassing the chemical accuracy threshold of 0.1 kcal/mol [7–9]. Extensive cross-platform benchmarking—including IBM Quantum, Qiskit Aer, and NVIDIA H100 clusters—demonstrates strong reproducibility, with inter-backend MAE variance below 3%. Positioned as a scalable and hardware-aware solution, MQPA offers practical advantages for quantum-enhanced molecular property prediction across applications in quantum chemistry, drug discovery, materials design, and environmental modeling.
Recent advances in quantum machine learning (QML) have opened new pathways for accelerating early-stage drug discovery through molecular representation in Hilbert space [1]–[3]. In this work, we present a hybrid quantum–classical framework that integrates quantum kernel estimation and variational quantum neural networks (QNNs) for ligand–target binding prediction [4], [5].A synthetic dataset reflecting the statistical behavior of the BindingDB database [6] was constructed to evaluate quantum descriptors and kernel performance under controlled conditions. The proposed quantum feature maps translate seven key molecular descriptors—molecular weight (MW), logP, hydrogen bond donors (HBD), acceptors (HBA), rotatable bonds (RB), aromatic rings (AR), and topological polar surface area (TPSA)—into entangled qubit states [7], [8].Comparative analyses with classical baselines (SVR, Random Forest, and deep neural networks) revealed that quantum embeddings achieve competitive predictive accuracy (RMSE ≈ 0.06) with improved stability under bootstrap resampling [9]. Quantum kernel alignment and sensitivity studies demonstrated that aromaticity and polarity jointly determine the representational power of QML models [10].Beyond performance, we emphasize Explainable Quantum Pharmacology (EQP)—a paradigm in which interpretability, reproducibility, and physicochemical causality are as essential as accuracy [11].Our findings establish a reproducible, interpretable, and computationally efficient foundation for hybrid QML pipelines in molecular modeling, paving the way for next-generation AI-driven drug discovery.