Quantum Computing (QC) offers outstanding potential for molecular characterization and drug discovery, particularly in solving complex properties like the Ground State Energy (GSE) of biomolecules. However, QC faces challenges due to computational noise, scalability, and system complexity. This work presents a hybrid framework combining Machine Learning (ML) techniques with quantum algorithms$-$Variational Quantum Eigensolver (VQE), Hartree-Fock (HF), and Quantum Phase Estimation (QPE)$-$to improve GSE predictions for large molecules. Three datasets (chemical descriptors, Coulomb matrices, and a hybrid combination) were prepared using molecular features from PubChem. These datasets trained XGBoost (XGB), Random Forest (RF), and LightGBM (LGBM) models. XGB achieved the lowest Relative Error (RE) of $4.41 \pm 11.18\%$ on chemical descriptors, outperforming RF ($5.56 \pm 11.66\%$) and LGBM ($5.32 \pm 12.87\%$). HF delivered exceptional precision for small molecules ($0.44 \pm 0.66\% RE$), while a near-linear correlation between GSE and molecular electron count provided predictive shortcuts. This study demonstrates that integrating QC and ML enhances scalability for molecular energy predictions and lays the foundation for scaling QC molecular simulations to larger systems.
Quantum Computing (QC) offers outstanding potential for molecular characterization and drug discovery, particularly in solving complex properties like the Ground State Energy (GSE) of biomolecules. However, QC faces challenges due to computational noise, scalability, and system complexity. This work presents a hybrid framework combining Machine Learning (ML) techniques with quantum algorithms−Variational Quantum Eigensolver (VQE), Hartree-Fock (HF), and Quantum Phase Estimation (QPE)−to improve GSE predictions for large molecules. Three datasets (chemical descriptors, Coulomb matrices, and a hybrid combination) were prepared using molecular features from PubChem. These datasets trained XGBoost (XGB), Random Forest (RF), and LightGBM (LGBM) models. XGB achieved the lowest Relative Error (RE) of 4.41±11.18% on chemical descriptors, outperforming RF (5.56±11.66%) and LGBM (5.32±12.87%). HF delivered exceptional precision for small molecules (0.44±0.66%RE), while a near-linear correlation between GSE and molecular electron count provided predictive shortcuts. This study demonstrates that integrating QC and ML enhances scalability for molecular energy predictions and lays the foundation for scaling QC molecular simulations to larger systems.
Drug discovery and molecular simulation are among the most promising near-term application domains for quantum computing, where electronic structure calculations -- scaling exponentially on classical computers via full configuration interaction -- become tractable through quantum phase estimation and variational quantum eigensolver (VQE) algorithms. This paper proposes the Quantum Drug Discovery Framework (QDDF), evaluating six quantum algorithms -- VQE, ADAPT-VQE, quantum phase estimation (QPE), quantum Monte Carlo (QMC), quantum machine learning for molecular property prediction, and quantum generative models for de novo drug design -- across 20 molecular benchmarks spanning H2 through FeMoco (54 qubits). Key results: ADAPT-VQE achieves chemical accuracy (< 1 kcal/mol) for molecules up to 24 qubits on NISQ hardware; QPE reaches ground-state energy error < 0.1 mHa for H2O on fault-tolerant models; quantum ML reduces molecular property prediction error by 22.4% versus classical graph neural networks. The framework introduces the Quantum Drug Discovery Utility Score (QDDUS) and quantum advantage thresholds for each task.
Correct prediction of the molecular ground-state energy is a fundamental problem in quantum chemistry, materials science and drug screening and discovery which directly impacts the understanding of the stability and chemical behavior of molecules. For complex molecular systems, traditional computational techniques like the Hartree-Fock and the Density Functional Theory (DFT) can be very costly. The improvement of molecular energy prediction efficiency and accuracy has recently opened new possibilities thanks to the progress in quantum computation and deep learning. In this paper, a hybrid Quantum Deep Learning Framework for Accurate Molecular Ground-State Energy Prediction in Complex Chemical Systems combining Variational Quantum Algorithms and Deep Neural Network Architectures is proposed. The framework builds molecular Hamiltonians, optimizes the quantum state with the Variational Quantum Eigensolver (VQE), and builds quantum-generated features for deep learning-based energy estimation. Performance measures used for the evaluation of the proposed model are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R²). The proposed Quantum Deep Learning Framework has shown Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R2 values equal to 0.042, 0.031, and 0.989 correspondingly, which proves higher performance from the standpoint of prediction. Prediction precision of the developed approach reaches 98.9% and exceeds that of all other methods known, including VQE, ADAPT-VQE, FermiNet, and Deep Schrödinger. Besides that, the proposed approach showed faster convergence, lower prediction variance, and higher computational efficiency during the molecular energies' prediction process. The results suggest that the proposed hybrid quantum-classical approach is promising and efficient for ground-state energy prediction of molecules, as well as useful in computational chemistry, materials discovery, and molecular design, and is scalable.
The pharmaceutical industry faces mounting challenges in drug discovery, where exploring a chemical space exceeding 10 60 possible compounds remains computationally prohibitive. Traditional in-silico screening approaches struggle to capture the quantum mechanical interactions that determine molecular behavior. To overcome these limitations, this study introduces a hybrid quantum–classical machine learning framework that uniquely integrates molecular graph theory with quantum-enhanced learning for accelerated drug discovery. Using the QM9 benchmark dataset containing 133,885 molecules of low-to-medium structural complexity, we extract Laplacian eigenvalue spectra as topological descriptors that preserve both connectivity and electronic information. These features are jointly processed by Random Forest (RF), Support Vector Machine (SVM), and a Variational Quantum Classifier (VQC) in a weighted ensemble architecture designed for balanced generalization. The Quantum Graph Neural Network (QGNN) component achieves 97.5% accuracy, while the hybrid ensemble attains 100.0% accuracy, confirming strong predictive reliability. Although quantum training requires roughly 3.2× longer computation, it captures high-dimensional correlations that classical methods overlook. Overall, the proposed framework bridges the gap between theoretical quantum computing and practical pharmaceutical modeling, providing a scalable, physics-inspired pathway toward intelligent molecular screening and next-generation drug discovery systems.
Accurate prediction of molecular structures and properties is vital for chemistry; materials science, and drug discovery, yet classical electronic-structure methods often fail for strongly correlated systems, large basis sets, and complex potential-energy landscapes. Quantum technology encompassing quantum computing, quantum machine learning, and hybrid quantum classical strategies offers a fundamentally new paradigm by encoding many-electron wave functions directly on qubits and exploiting superposition and entanglement to explore exponentially large Hilbert spaces. This review synthesizes recent algorithmic and hardware advances relevant to molecular modelling, including the Variational Quantum Eigensolver (VQE), Quantum Phase Estimation (QPE), quantum unitary coupled-cluster (q-UCC) families, equation-of-motion and subspace methods for excited states, tensor-network hybrids, and quantum kernel and variational QML approaches. We examine noise-aware hybrid workflows, error-mitigation techniques, symmetry-preserving ansätze, and operator-factorization methods that reduce measurement and gate overhead. Representative applications are discussed: ground- and excited-state energy prediction, potential-energy surface mapping, geometry and transition-state optimization, spectroscopic property estimation (IR, UV–Vis, NMR, EPR), and reaction-dynamics scenarios where non-adiabatic effects and conical intersections dominate. Resource estimates and scaling analyses clarify current NISQ limitations qubit counts, circuit depth, shot complexity and delineate the roadmap to fault-tolerant QPE for chemical accuracy. We compare quantum approaches with classical baselines (DFT, CCSD (T), multireference methods), identifying domains where quantum methods already show promise (strong correlation, multi-reference dissociation, spin-state ordering) and where classical methods remain competitive. Finally, we highlight near-term industrial opportunities in drug design, catalysis, CO2 capture, and energy materials, and outline critical research directions: algorithmic reductions in measurement/precision cost, hardware improvements in fidelity and connectivity, scalable ansatz design, and integrated software stacks for reproducible hybrid simulations. Together, these developments indicate that while practical, large-scale quantum advantage for general chemistry remains future work, quantum technologies are rapidly maturing into powerful tools for targeted molecular problems that are intractable with existing classical techniques.
Quantum machine learning (QML) has been demonstrated to have great potential in revolutionizing the process of drug discovery by dealing with the problem of high computational complexity of molecular simulations. Conventional computational techniques are not very effective in predicting the properties of molecules and pharmaceutical activity because the simulation of large molecules is too complex. This paper will discuss how QML algorithms can be used, specifically variational quantum circuits and quantum kernel methods, to improve the predictability of molecular properties and accelerate the process of drug discovery. To enhance the prediction accuracy, the research use quantum-enhanced feature extraction together with classical machine learning technologies. Research methodology is applied to datasets of molecular structure and biological activity in order to provide a more useful model to learn about how molecular features relate to biological outcomes using quantum machine learning models. These results suggest that it is 15 % more accurate in prediction and 20 % faster in computing high-dimensional molecular simulations compared to standard machine learning algorithms. The efficiency of the QML-based method is statistically proven with an accuracy 89%, precision 87%, and an F1-score of 0.91. The results point to the possible ability of QML to decrease the time and computational resources spent on the exploration of drug discovery, providing a new direction for the improvement of molecular property prediction. The study is a contribution to the emerging area of QML in pharmaceutical use and demonstrates that it can simplify drug discovery and enhance the accuracy with which predictions are made. The paper demonstrates that QML may produce a significant impact on the drug development process by simulating the molecular properties faster, more effectively, and more accurately.
Accurate and efficient prediction of electronic wavefunctions is central to ab initio molecular dynamics (AIMD) and electronic structure theory. However, conventional ab initio methods require self-consistent optimization of electronic states at every nuclear configuration, leading to prohibitive computational costs, especially for large or strongly correlated systems. Here, we introduce a quantum-centric machine learning (QCML) model-a hybrid quantum-classical framework that integrates parameterized quantum circuits (PQCs) with Transformer-based machine learning to directly predict molecular wavefunctions and quantum observables. By pretraining the Transformer on a diverse dataset of molecules and ansatz types and subsequently fine-tuning it for specific systems, QCML learns transferable mappings between molecular descriptors and PQC parameters, eliminating the need for iterative variational optimization. The pretrained model achieves chemical accuracy in potential energy surfaces, atomic forces, and dipole moments across multiple molecules and ansatzes, and enables efficient AIMD simulations with infrared spectra prediction. This work establishes a scalable and transferable quantum-centric machine learning paradigm, bridging variational quantum algorithms and modern deep learning for next-generation molecular simulation and quantum chemistry applications.