Accurate prediction of binding energies between drug candidates and target molecules is essential in computational drug discovery, as stronger (more negative) binding energies suggest higher efficacy. This energy corresponds to the ground state of the molecular system and can be computed using methods such as Full Configuration Interaction (FCI), Hartree-Fock (HF), Variational Quantum Eigensolver (VQE), and Quantum Phase Estimation (QPE). While FCI offers benchmark accuracy, its computational cost renders it impractical for larger systems. HF scales efficiently but at the expense of precision. QPE, though theoretically robust, is currently constrained by hardware limitations. VQE emerges as a practical alternative for Noisy Intermediate-Scale Quantum (NISQ) devices, balancing accuracy and feasibility. Nevertheless, quantum noise significantly affects VQE performance. To address this, we incorporate Zero Noise Extrapolation (ZNE) to mitigate errors and improve energy estimates. This study compares these approaches and demonstrates the efficacy of the VQEZNE framework in generating reliable binding energy calculations for drug design applications.
Accurate prediction of binding energies between drug candidates and target molecules is essential in computational drug discovery, as stronger (more negative) binding energies suggest higher efficacy. This energy corresponds to the ground state of the molecular system and can be computed using methods such as Full Configuration Interaction (FCI), Hartree-Fock (HF), Variational Quantum Eigensolver (VQE), and Quantum Phase Estimation (QPE). While FCI offers benchmark accuracy, its computational cost renders it impractical for larger systems. HF scales efficiently but at the expense of precision. QPE, though theoretically robust, is currently constrained by hardware limitations. VQE emerges as a practical alternative for Noisy Intermediate-Scale Quantum (NISQ) devices, balancing accuracy and feasibility. Nevertheless, quantum noise significantly affects VQE performance. To address this, we incorporate Zero Noise Extrapolation (ZNE) to mitigate errors and improve energy estimates. This study compares these approaches and demonstrates the efficacy of the VQEZNE framework in generating reliable binding energy calculations for drug design applications.
Classical simulation of molecular electronic structure is often limited by the rapid increase in computational cost associated with describing electron correlation. The Variational Quantum Eigensolver (VQE) is one hybrid quantum-classical approach ideally adapted to the Noisy Intermediate-Scale Quantum (NISQ) regime. This work applies the VQE algorithm to compute ground state energies of a set of molecules:H2,H3+,LiH,HF,OH−,H2O,BH3,CH4, andNH3. The approach utilizes the Unitary Coupled Cluster for Single and Double excitations (UCCSD) ansatz along with the Bravyi-Kitaev transformation for fermionic-to-qubit encoding. Quantum simulation outputs are systematically benchmarked against Full Configuration Interaction (FCI), a numerically exact traditional approach, and Unrestricted Hartree-Fock (UHF). The results establish a robust consistency of the VQE-obtained molecular ground state energies against standard FCI and UHF benchmarks. This research provides a critical benchmark, confirming that VQE has the capability for high-precision molecular simulations and playing the role of a pivotal stepping stone toward revolutionizing applications in drug discovery and materials science.
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.
molecule, emphasizing practical implementation and performance on current quantum hardware. Our research presents a comparative study of HEA and UCCSD ansätze on noiseless and noisy simulations and implements VQE on recent IBM quantum computer noise models and a real quantum computer, IBM Fez, providing a fully functional code employing Qiskit 1.2. Our experiments confirm UCCSD's reliability in ideal conditions, while the HEA demonstrates greater robustness to hardware noise, achieving chemical accuracy on state-vector simulation (SVS). The results reveal that achieving ground-state energy within chemical accuracy is feasible without error mitigation during VQE convergence. We demonstrate that current quantum devices effectively optimize circuit parameters despite misestimating simulated energies. The SVS-evaluated energies provide a more accurate representation of the solution quality compared to QPU-estimated energy values, indicating that VQE converges to the correct ground state despite quantum noise. Our study also applies noise mitigation as a postprocessing technique, using zero-noise extrapolation (ZNE) on a real quantum computer. The detailed methodologies presented in this study, including Hamiltonian construction and Fermionic-to-qubit transformations, facilitate flexible adaptation of the VQE approach for various algorithm variants and across different levels of algorithmic implementation.
Cancer drug discovery is a resource-intensive process characterized by low success rates, protracted timelines, and significant cost implications. Conventional screening methods—including high-throughput assays and classical molecular modeling—struggle to capture the quantum nature of biomolecular interactions critical to binding affinity and drug specificity. In response, quantum molecular simulation (QMS) has emerged as a transformative approach that leverages the principles of quantum mechanics to enhance the accuracy and efficiency of drug-target interaction modeling. This review explores the theoretical foundations, computational methodologies, and real-world applications of QMS in cancer drug discovery. It discusses key quantum approaches such as Density Functional Theory (DFT), Hartree-Fock (HF), and hybrid QM/MM methods, while evaluating the role of quantum algorithms— including Variational Quantum Eigensolvers (VQE) and Quantum Phase Estimation (QPE)—in elucidating biomolecular structures and energetics. The integration of QMS with next-generation quantum hardware platforms (e.g., superconducting qubits and quantum annealers) and open-source software ecosystems is also reviewed. Comparative performance analyses highlight the advantages of QMS over classical methods in terms of precision, scalability, and its potential for personalized oncology applications. Nonetheless, significant challenges remain, including issues of decoherence, algorithmic noise, regulatory integration, and reproducibility. This paper presents a forward-looking perspective on how QMS, when synergized with artificial intelligence and omics data, could fundamentally reshape the paradigm of cancer therapeutic development by enabling faster, more accurate, and mechanism-driven drug discovery.
Variational quantum eigensolvers (VQEs) are among the most promising quantum algorithms for solving electronic structure problems in quantum chemistry, particularly during the noisy intermediate-scale quantum (NISQ) era. In this study, we investigate the capabilities and limitations of VQE algorithms implemented on current quantum hardware for determining molecular ground-state energies, focusing on the adaptive derivative-assembled pseudo-Trotter ansatz VQE (ADAPT-VQE). To address the significant computational challenges posed by molecular Hamiltonians, we explore various well known strategies to simplify the Hamiltonian, optimize the ansatz, and improve classical parameter optimization through modifications of the COBYLA optimizer. These enhancements are integrated into a tailored quantum computing implementation designed to minimize the circuit depth and computational cost. Using benzene as a benchmark system, we demonstrate the application of these optimizations on an IBM quantum computer. Despite these improvements, our results highlight the limitations imposed by current quantum hardware, particularly the impact of quantum noise on state preparation and energy measurement. The noise levels in today's devices prevent meaningful evaluations of molecular Hamiltonians with sufficient accuracy to produce reliable quantum chemical insights. Finally, we extrapolate the requirements for future quantum hardware to enable practical and scalable quantum chemistry calculations using VQE algorithms. This work provides an assessment of current quantum algorithms for molecular modeling on real quantum hardware, highlighting the impact of noise and hardware limitations on the achievable accuracy.
In this research paper, we explore the application of quantum computing technologies, specifically the Variational Quantum Eigensolver, to accurately determine the ground state energies of small molecules, especially for molecules with higher bond orders. Our study showcases the feasibility and accuracy of quantum algorithms in performing complex quantum mechanical calculations for molecules like hydrogen, neon, water, methane, nitrogen, oxygen, ethyne, and ethene. Our results demonstrate high precision and reliability in molecular simulations with errors from theoretical values of less than 2 percent for most of the molecules. We also address the challenges associated with scalability and error rates when dealing with larger and more complex molecules. By combining the power of quantum computing with molecular simulations, we aim to enhance our understanding of molecular behavior, leading to more precise predictions in material science and drug discovery.