Accurate prediction of bond dissociation energies (BDEs) underpins mechanistic insight and the rational design of molecules and materials. We present a systematic, reproducible benchmark comparing quantum and classical machine learning models for BDE prediction using a chemically curated feature set encompassing atomic properties (atomic numbers, hybridization), bond characteristics (bond order, type), and local environmental descriptors. Our quantum framework, implemented in Qiskit Aer on six qubits, employs ZZFeatureMap encodings with variational ansätz (RealAmplitudes) across multiple architectures Variational Quantum Regressors (VQR), Quantum Support Vector Regressors (QSVR), Quantum Neural Networks (QNN), Quantum Convolutional Neural Networks (QCNN), and Quantum Random Forests (QRF). These are rigorously benchmarked against strong classical baselines, including Support Vector Regression (SVR), Random Forests (RF), and Multi-Layer Perceptrons (MLP). Comprehensive evaluation spanning absolute and relative error metrics, threshold accuracies, and error distributions shows that top-performing quantum models (QCNN, QRF) match the predictive accuracy and robustness of classical ensembles and deep networks, particularly within the chemically prevalent mid-range BDE regime. These findings establish a transparent baseline for quantum-enhanced molecular property prediction and outline a practical foundation for advancing quantum computational chemistry toward near-chemical accuracy.
Accurate prediction of bond dissociation energies (BDEs) underpins mechanistic insight and the rational design of molecules and materials. We present a systematic, reproducible benchmark comparing quantum and classical machine learning models for BDE prediction using a chemically curated feature set encompassing atomic properties (atomic numbers, hybridization), bond characteristics (bond order, type), and local environmental descriptors. Our quantum framework, implemented in Qiskit Aer on six qubits, employs ZZFeatureMap encodings with variational ansätz (RealAmplitudes) across multiple architectures Variational Quantum Regressors (VQR), Quantum Support Vector Regressors (QSVR), Quantum Neural Networks (QNN), Quantum Convolutional Neural Networks (QCNN), and Quantum Random Forests (QRF). These are rigorously benchmarked against strong classical baselines, including Support Vector Regression (SVR), Random Forests (RF), and Multi-Layer Perceptrons (MLP). Comprehensive evaluation spanning absolute and relative error metrics, threshold accuracies, and error distributions shows that top-performing quantum models (QCNN, QRF) match the predictive accuracy and robustness of classical ensembles and deep networks, particularly within the chemically prevalent mid-range BDE regime. These findings establish a transparent baseline for quantum-enhanced molecular property prediction and outline a practical foundation for advancing quantum computational chemistry toward near-chemical accuracy.
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) offer new opportunities for chemical property prediction, yet their practical performance relative to established classical models remains poorly characterized on real-world datasets. Building on our previous benchmark of classical and deep learning models for collision cross-section (CCS) predictions, we present an empirical comparison of quantum kernel regression (QKR) and a variational quantum regressor (VQR) against classical machine learning (CML) techniques, including Random Forest (RF), support vector regression (SVR), Lasso, and a Voting Regressor (VR) ensemble. Using an aquatic dissolved organic matter (DOM) dataset with five molecular descriptors and five chemical classes, we systematically investigate QKR feature map repetitions and classical readout kernels, as well as VQR circuit depths and optimizer choices, to identify performance-optimal QML configurations. Regression performance is evaluated using the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) across training-set sizes up to 600 molecules per class. Overall, RF and Lasso deliver the most robust CCS predictions, achieving R2 ≥ 0.9 for lignins, carbohydrates, lipids, and proteins. Meanwhile, the optimized QKR architecture exhibits substantial gains as data increase and achieves competitive performance on chemically homogeneous subsets such as carbohydrates and lipids. In contrast, the VQR ansatz remains consistently weaker than both QKR and the classical baselines across all classes and data sizes. The results highlight the strong influence of dataset size, feature representation, and molecular heterogeneity on QML performance while also identifying conditions under which quantum methods may become competitive in future studies.
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.
Predicting drug-target binding affinity with limited training data remains a central challenge in computational drug discovery. We introduce a hybrid quantum-classical framework combining molecular descriptors with variational quantum circuits for interpretable binding affinity prediction. Seven physicochemical descriptors (MW, logP, TPSA, HBD/HBA, rotatable bonds, aromatic rings) were encoded into 6-qubit variational circuits using parameterized rotations and controlled-Z entanglement. Quantum kernels were extracted and used for regression on 1,200 BindingDB molecules across kinase, GPCR, and protease targets, with external validation on 300 ChEMBL compounds. Performance was compared against SVR, random forest, gradient boosting, and neural network baselines using 100-fold bootstrap validation. Variational quantum regression achieved MSE = 0.056 ± 0.009, outperforming SVR by 32% (0.082 ± 0.013), RF by 28%, and GBM by 24%. Quantum advantage was most significant in low-data regimes (n < 500), where VQR maintained R2 > 0.85 versus R2 < 0.72 for classical methods. External validation confirmed generalization (R2 = 0.83). Our Explainable Quantum Pharmacology framework revealed TPSA and logP as dominant predictive features through gradientbased sensitivity analysis, aligning with established medicinal chemistry principles. Quantum kernels provide measurable, chemically interpretable improvements for molecular property prediction in data-limited scenarios. This work demonstrates practical quantum advantage for early-stage drug discovery applications on near-term quantum hardware.
Accurate machine-learning (ML) prediction of material energies typically relies on large models with thousands to hundreds of thousands of parameters and scales poorly with the system size. Here, we present a compact hybrid classical-quantum ML framework that predicts the energies of complex materials using fewer than ten qubits, regardless of system size. By compressing local atomic descriptors into a fixed-size feature spectrum, we enable amplitude encoding of chemically diverse systems into shallow quantum circuits. We implement quantum convolutional neural networks (QCNNs) and quantum deep neural networks (QDNNs) with fewer than 100 trainable parameters and achieve prediction errors below 0.05 eV/atom on chemically complex alloys ( Co 25 Ni 25 ( Hf Ti Zr ) 50 ) and lithium-intercalated metal-organic frameworks. Our results demonstrate the feasibility of scalable quantum assisted materials modeling and introduce a resource-efficient route toward quantum machine learning for atomistic simulations.
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.