Researchers from Cleveland Clinic, RIKEN, and IBM have carried out the largest quantum-classical chemistry simulation to date, modeling protein-ligand systems with more than 12,000 atoms. The work marks a major scale-up in how quantum computers can be used alongside classical supercomputers to study real-world chemistry problems.

The team simulated two biologically relevant proteins, T4-Lysozyme and Trypsin, along with the molecules they bind to, in a realistic water environment. The largest system reached 12,635 atoms and roughly 30,000 orbitals, pushing far beyond earlier quantum computing demonstrations in chemistry.

This result comes just months after researchers modeled a much smaller 303-atom protein. The new work represents a 40-fold increase in system size and a 210-times improvement in accuracy in a key part of the workflow, highlighting rapid progress in the field.

To achieve this, the researchers combined quantum processors with high-performance classical systems, creating what they describe as a quantum-centric supercomputing workflow. Quantum hardware handled the most complex parts of the calculation, while classical supercomputers stitched the results together.

Quantum meets real chemistry

The team used up to 94 qubits across two quantum processors to perform sampling, running 9,200 circuits over more than 100 hours and collecting 1.3 billion measurement outcomes. The quantum data was then processed using powerful classical systems, including Japan’s Fugaku supercomputer.

“This result is one of those things you dream about,” said Dr. Kenneth Merz, who led the study.

The approach builds on a method that breaks large molecules into smaller, manageable clusters. Classical computers solve simpler regions, while quantum systems tackle the most entangled and computationally difficult parts. The results are then recombined to produce an overall picture of the molecule.

Researchers also introduced improvements to both classical and quantum techniques. One key step involved refining how the system identifies which parts of a molecule need detailed quantum treatment, reducing the overall computational cost.

Scaling up quantum workflows

Another advance came from a new quantum algorithm that improves how relevant electronic configurations are identified. This helps the system focus on the most important parts of a molecule’s behavior while ignoring less useful data.

Despite the progress, the method does not yet outperform the best classical approaches. However, it demonstrates that quantum systems can already contribute to meaningful scientific problems, particularly when integrated with existing computing infrastructure.

“If we want another order-of-magnitude-or-two bump, quantum computing is probably the way to go,” Merz said.

The findings suggest that hybrid quantum-classical workflows could become a practical tool for chemistry, especially as quantum hardware continues to improve. Future systems are expected to handle even larger and more complex molecules with greater accuracy.

The potential applications are significant. More accurate simulations could speed up drug discovery, improve materials design, and reduce the need for costly laboratory experiments.

The research highlights how combining quantum processors with classical computing resources may define the next phase of high-performance computing, offering a path toward solving problems that are currently out of reach.