A research team spanning Cleveland Clinic, Japan’s RIKEN institute, and IBM has used a hybrid quantum-classical computing framework to simulate a biologically meaningful protein containing 12,635 atoms — described by the team as the largest molecular system of its kind ever modeled with the help of quantum computers. The work is a finalist for the 2026 ACM Gordon Bell Prize, one of the most prestigious honors in high-performance computing.
Rather than attempting to run the entire simulation on quantum hardware — which remains limited in scale and prone to error — the team combined quantum processors with traditional high-performance computing resources, using each type of hardware for the part of the calculation it handles best. That hybrid approach has become an increasingly common strategy as researchers look for practical, near-term applications of quantum computing ahead of the arrival of larger, more error-resistant quantum machines.
Simulating large proteins accurately is a longstanding challenge in computational biology, with major implications for drug discovery, since understanding how a protein folds and interacts with other molecules can reveal potential targets for new medicines. Classical supercomputers can struggle with the sheer complexity of modeling molecular interactions at this scale, which is part of why quantum-assisted approaches are drawing serious interest from pharmaceutical researchers.
While practical, everyday use of quantum computing in drug discovery is still some way off, results like this one are seen as important proof points that hybrid quantum-classical methods can already tackle real-world scientific problems larger than what was possible just a few years ago.