We introduce an approach to sampling equilibrium solvent water molecule configurations within proteins that leverages analog quantum computing. We present a complete end-to-end study from the molecular biology application to the development of the quantum algorithm to the implementation on a neutral atom quantum processing unit (QPU). To do so, we combine a quantum placement strategy to the 3D Reference Interaction Site Model, an approach capable of predicting continuous solvent distributions. The intrinsic quantum nature of such coupling guarantees molecules not to be placed too close to each other, a constraint usually imposed by hand in classical approaches. We present first a full quantum adiabatic evolution model that uses a local Rydberg Hamiltonian to cast the general problem into an antiferromagnetic Ising model. Its solution is embodied into a Rydberg atom array QPU. Following a classical emulator implementation, a QPU portage allows to experimentally validate the algorithm performances on an actual quantum computer. As a perspective of use on next generation devices, we emulate a second hybrid quantum-classical version of the algorithm. Such a variational quantum approach uses a classical Bayesian minimization routine to find the optimal laser parameters. Overall, these Quantum-3D-RISM algorithms open a route towards the application of analog quantum computing in molecular modeling and drug design. Published by the American Physical Society 2024
We introduce an approach to sampling equilibrium solvent water molecule configurations within proteins that leverages analog quantum computing. We present a complete end-to-end study from the molecular biology application to the development of the quantum algorithm to the implementation on a neutral atom quantum processing unit (QPU). To do so, we combine a quantum placement strategy to the 3D Reference Interaction Site Model, an approach capable of predicting continuous solvent distributions. The intrinsic quantum nature of such coupling guarantees molecules not to be placed too close to each other, a constraint usually imposed by hand in classical approaches. We present first a full quantum adiabatic evolution model that uses a local Rydberg Hamiltonian to cast the general problem into an antiferromagnetic Ising model. Its solution is embodied into a Rydberg atom array QPU. Following a classical emulator implementation, a QPU portage allows to experimentally validate the algorithm performances on an actual quantum computer. As a perspective of use on next generation devices, we emulate a second hybrid quantum-classical version of the algorithm. Such a variational quantum approach uses a classical Bayesian minimization routine to find the optimal laser parameters. Overall, these Quantum-3D-RISM algorithms open a route towards the application of analog quantum computing in molecular modeling and drug design. Published by the American Physical Society 2024
Demonstrating the practical utility of Noisy Intermediate-Scale Quantum (NISQ) hardware for recurrent tasks in Computer-Aided Drug Discovery is of paramount importance. We tackle this challenge by performing three-dimensional protein pockets hydration-site prediction on a quantum computer. Formulating the water placement problem as a Quadratic Unconstrained Binary Optimization (QUBO), we use a hybrid approach coupling a classical three-dimensional reference-interaction site model (3D-RISM) to an efficient quantum optimization solver, to run various hardware experiments up to 123 qubits. Matching the precision of classical approaches, our results reproduced experimental predictions on real-life protein-ligand complexes. Furthermore, through a detailed resource estimation analysis, we show that accuracy can be systematically improved with increasing number of qubits, indicating that full quantum utility is in reach. Finally, we provide evidence that advantageous situations could be found for systems where classical optimization struggles to provide optimal solutions. The method has potential for assisting simulations of protein-ligand complexes for drug lead optimization and setup of docking calculations.
Analog quantum processors based on Rydberg atom arrays are a powerful platform for many-body quantum simulation, combinatorial optimization, and graph machine learning. As these devices become increasingly accessible, establishing confidence in their outputs requires predictive models that quantitatively connect microscopic hardware imperfections to empirical results. Here, we present a noise-aware emulation framework that propagates the dominant noise mechanisms throughout the full computation cycle to predict device behavior. We validate the framework by benchmarking two representative protocols, quantum annealing and post-quench dynamics, on three Pasqal quantum processors where classical simulations still provide ground truth. Across all three devices, the measured observables fall within the uncertainty envelopes predicted by the emulator. Beyond reproducing the data, the framework isolates which physical mechanism dominates in each operating regime, provides quantitative guidance for algorithm design and hardware improvements, and establishes a foundation for verifying analog processors in regimes beyond classical reach.
• Published on arXiv, successfully utilized actual quantum hardware with up to 123 qubits to predict hydration sites in three-dimensional protein pockets. • Formulated water placement as a Quadratic Unconstrained Binary Optimization (QUBO) problem, achieving precision matching classical computing by combining a classical 3D-RISM model with a quantum solver. • Confirmed that prediction accuracy systematically improves as the number of qubits increases, proving the practical utility of quantum computing in next-generation lead optimization and docking calculations. [Quantum Biology Society] In Computer-Aided Drug Discovery (CADD), accurately predicting how drugs and water molecules interact and are placed inside a target protein pocket—that is, the hydration site—plays a crucial role in evaluating drug binding affinity. However, long-standing questions have remained regarding whether current Noisy Intermediate-Scale Quantum (NISQ) hardware can solve such complex and recurrent biological optimization tasks at a practical level. Recently, a study tackling this limitation head-on by mobilizing an actual 123-qubit quantum computer was published on the preprint repository arXiv, drawing significant attention. This paper, titled "Practical protein-pocket hydration-site prediction for drug discovery on a quantum computer," shows that quantum computing can be deployed in earnest in the field of drug design. A joint research team including Dr. Daniele Loco from Qubit Pharmaceuticals in France, Professor Jean-Philip Piquemal from Sorbonne Université, and the quantum control company Q-CTRL achieved this groundbreaking result. ■ Replacing Water Placement with a Quantum Optimization Problem The research team newly formulated the optimal water placement problem inside a protein as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Instead of leaving the entire computation solely to quantum hardware, they adopted a hybrid approach coupling a classical three-dimensional reference-interaction site model (3D-RISM) with an efficient quantum optimization solver. Based on this, they performed various hardware experiments utilizing up to 123 qubits in an actual quantum hardware environment. As a result, they succeeded in reproducing a precision equivalent to state-of-the-art classical approaches in predicting the hydration sites of real-life protein-ligand complexes. ■ Evolving Accuracy as Qubits Increase Another significant achievement of this study is proving a distinct correlation between the increase in quantum resources (number of qubits) and prediction accuracy. Through a detailed resource estimation analysis, they confirmed that the accuracy of hydration-site prediction systematically improves as the number of mobilized qubits increases. This means that even in massive and complex biomolecular systems where classical optimization algorithms hit computational limits, quantum computers can reach full quantum utility as devices scale in the near term. This achievement strongly suggests that the quantum-classical hybrid hydration prediction model will become a groundbreaking tool that changes the landscape of next-generation lead optimization processes and precise drug docking calculations.
New computational strategies, such as molecular docking, are emerging to speed up the drug discovery process. This method predicts the activity of molecules at the binding site of proteins, helping to select the ones that exhibit desirable behavior and rejecting the rest. However, for large chemical libraries, it is essential to search and score configurations using fewer computational resources while maintaining high precision. In this work, we map the molecular docking problem to a graph problem, a maximum-weight independent set problem on a unit-disk graph in a physical neutral atom quantum processor. Here, each vertex represents an atom trapped by optical tweezers. The Variational Quantum Adiabatic Algorithm (VQAA) approach is used to solve the generic graph problem with two optimization methods, Scipy and Hyperopt. Additionally, a machine learning method is explored using the adiabatic algorithm. Results for multiple graphs are presented, and a small instance of the molecular docking problem is solved, demonstrating the potential for near-term quantum applications.
Accurate prediction of binding affinities and efficient lead optimization remain central challenges in computational drug discovery, particularly for complex biological targets characterized by rugged energy landscapes, conformational heterogeneity, and high-dimensional interaction spaces. Classical computational methods—such as molecular docking, force-field–based simulations, and deep learning models—have made substantial progress, yet they often struggle to capture quantum-mechanical effects and long-range correlations critical to molecular recognition. This study explores a quantum-enhanced framework that integrates Variational Quantum Algorithms (VQAs), quantum-inspired Hamiltonian modeling, and hybrid quantum–classical optimization workflows to improve the fidelity and scalability of drug discovery pipelines. By encoding molecular interactions into parameterized quantum circuits, the framework leverages quantum superposition and entanglement to explore complex chemical space more efficiently, enabling refined estimation of binding energies and accelerated identification of high-quality leads. Preliminary simulations indicate that VQA-based estimators outperform classical baselines for challenging protein–ligand systems, reducing prediction error while maintaining computational tractability. These findings highlight the emerging potential of quantum technologies to enable more accurate, data-efficient, and mechanistically grounded drug discovery for next-generation therapeutics.
We present a combination of three-dimensional reference interaction site model self-consistent field (3D-RISM-SCF) theory and the variational quantum eigensolver (VQE) to consider the solvent distribution effects within the framework of quantum-classical hybrid computing. The present method, 3D-RISM-VQE, does not include any statistical errors from the solvent configuration sampling owing to the analytical treatment of the statistical solvent distribution. We apply 3D-RISM-VQE to compute the spatial distribution functions of solvent water around a water molecule, the potential and Helmholtz energy curves of NaCl, and to analyze the Helmholtz energy component and related properties of H 2 O and NH 4 + . Moreover, we utilize 3D-RISM-VQE to analyze the extent to which solvent effects alter the efficiency of quantum calculations compared with calculations in the gas phase using the L 1 -norms of molecular electronic Hamiltonians. Our results demonstrate that the efficiency of quantum chemical calculations on a quantum computer in solution is virtually the same as that in the gas phase.