Quantum chemistry studies report advances in molecular simulations for drug-like molecules

Quantum chemistry researchers reported two advances in molecular simulation, with one team demonstrating chemically accurate quantum simulations of complex molecular systems on superconducting quantum hardware and another refining an orbital-free computing process with machine learning to deliver precise results and a physically meaningful solution. The studies extend simulations beyond small benchmark molecules and report performance on considerably larger drug-like molecules.

Working on IQM’s 24-qubit Sirius processor, an international team led by Qclairvoyance Quantum Labs successfully simulated molecular systems using up to 16 qubits and introduced a hybrid quantum–high performance computing workflow that enables scalable and accurate results on near-term quantum devices. Among the key achievements, the researchers experimentally generated a full two-dimensional potential energy surface for a water molecule directly on quantum hardware and simulated the FDA-approved drug Amantadine, showcasing the ability to handle pharmacologically relevant molecules beyond small benchmark systems.

To overcome the limitations of current quantum processors, the team used Sample-based Quantum Diagonalization, in which the quantum processor is used for sampling key electronic configurations while computationally intensive calculations are offloaded to classical supercomputers. For larger molecules like Amantadine, the researchers combined SQD with Density Matrix Embedding Theory, enabling them to break down complex systems into smaller, quantum-computable fragments while preserving chemical accuracy. The study also explores different quantum circuit strategies, providing insights into balancing computational cost and accuracy, and the work is detailed in an arXiv preprint.

In a separate study, two research teams at the Interdisciplinary Center for Scientific Computing refined a computing process long held to be unreliable such that it delivers precise results and reliably establishes a physically meaningful solution. The findings are published in the Journal of the American Chemical Society. The new process, STRUCTURES25, is based on a specifically developed neural network that learns the relationship between electron density and energy directly from precise reference calculations, capturing the chemical environment of each individual atom in a mathematically detailed representation.

A unique training concept was pivotal: the model was trained not only with converged electron densities, but also with many variants surrounding the correct solution, generated by targeted, controlled changes in the underlying reference calculations. In tests on a large and diverse collection of organic molecules, STRUCTURES25 achieved a precision that can compete with established reference calculations, for the first time demonstrating a stable convergence using an orbital-free approach. The performance of the method was demonstrated not only on small examples, but on considerably larger drug-like molecules as well.

Initial runtime comparisons prove that the computing process can scale better with growing molecule size and hence increase the speed of the calculation. Calculations formerly considered too elaborate are now within reach. Both studies describe approaches aimed at scalable molecular simulation for applications including drug discovery, materials science, better batteries, materials for energy conversion, and more efficient catalysts.