Insider Brief
- Cleveland Clinic Clinic, RIKEN and IBM researchers were named finalists for the 2026 ACM Gordon Bell Prize after using quantum and classical computers to simulate biologically meaningful molecular systems containing as many as 12,635 atoms.
- The team combined IBM Quantum Heron processors with the Fugaku, Miyabi-G and ROQUO supercomputers to calculate protein structures and binding energies.
- The approach could help researchers model how potential medicines interact with protein targets while reducing computational demands, manual data transfers and errors.
PRESS RELEASE — A multidisciplinary team from Cleveland Clinic, RIKEN and IBM (NYSE: IBM ) has been named as a finalist for the 2026 Association for Computing Machinery (ACM) Gordon Bell Prize for simulating the largest biologically meaningful molecules ever modeled with quantum computers, reaching a scale of 12,635 atoms. To achieve these results, the team united the strengths of quantum and classical computing methods in a framework known as quantum-centric supercomputing.
The ACM Gordon Bell Prize recognizes outstanding achievement in high-performance computing and is one of the field’s most prestigious honors. The 2026 winner will be announced at the International Conference for High-Performance Computing, Networking, Storage, and Analysis (SC26), taking place Nov. 15–20, 2026 in Chicago.
The origins of the project are rooted in the team’s investigation into how computation in drug discovery can be improved. Doing so rests on two fundamental challenges: first, modeling the movement of atoms as biological processes unfold; and second, accurately computing their energies. Particularly the second challenge is well-suited for quantum computers, which operate by the laws of quantum mechanics.
In the work, initially published in May 2026, the team calculated the electronic structure of two large protein complexes using the IBM Quantum Heron processors running within the IBM quantum computers at Cleveland Clinic in the United States and RIKEN in Japan. The calculations were executed alongside two of the world’s most powerful supercomputers, Fugaku at RIKEN and Miyabi-G, which is operated by the University of Tokyo and University of Tsukuba. The quantum computers used up to 94 qubits to run nearly 6,000 quantum operations within certain parts of the problem, which was essential to the computation’s accuracy and success. Fugaku and Miyabi-G were used to reassemble the results and allow the team to gain a complete representation of each molecule.
The results reflect the team’s innovative approach alongside the rapid maturation of quantum computing. Using the sample-based quantum diagonalization by IBM and RIKEN (featured on the cover of Science Advances) along with embedded wavefunction methods adapted by Cleveland Clinic to the question at hand, the team was able to report the first-known simulation of a 303-atom protein achieved with quantum computers. Less than a year later, the team first scaled their method roughly 40 times while also achieving 210 times improvement in accuracy. In updated results recently published in September, the team advanced the work even further. Most notably, they were able to further improve the accuracy of their computations of the binding energies of the molecular system, which reflect how tightly the molecules are bound together and can predict how they could interact with other systems.
In addition, the team validated the workflow on a third supercomputer, JHPC-quantum GPU supercomputer “ ROQUO ,” RIKEN’s newest system in a way that eliminated the need for complex manual operations and data transfers – pointing toward faster, more accessible research. By orchestrating CPUs, GPUs, and QPUs together, the team minimized the need for manual transfers and further reduced errors — an early demonstration of how classical and quantum computing can work in concert on complex scientific problems.
Taken together with the earlier results, these updates reflect continued progress on both the accuracy of computed binding energies and the time-to-solution enabled by the automated workflow.
The quantum-classical techniques developed by the team continues to reduce the computational overhead required to directly represent the chemistry of molecular systems with accuracy and is pushing the frontiers of what is possible with quantum-centric supercomputing in the field. The work has demonstrated a path to further increase the accuracy of how molecular system can be calculated and is a step towards helping researchers better predict how medicines may interact with protein targets.
The research team includes Kenneth Merz Jr, Akhil Shajan, Danil Kaliakin, Fangchun Liang of Cleveland Clinic, Yuichi Otsuka, Tomonori Shirakawa, Lukas Broers, Han Xu, Miwako Tsuji, Mitsuhisa Sato, Seiji Yunoki of RIKEN Center for Computational Science, and Ryo Wakizaka, Yukio Kawashima, Jun Doi, Hitomi Takahashi, Toshinari Itoko, Hiroshi Horii, Thaddeus Pellegrini, Javier Robledo Moreno, Kevin J. Sung, Ella Fejer, Robert Walkup, Seetharami Seelam, Mario Motta of IBM.
To read the full study, visit: https://arxiv.org/abs/2605.01138 .
Research Support
This research is supported by NEDO (New Energy and Industrial Technology Development Organization), an organization under the jurisdiction of Japan’s Ministry of Economy, Trade and Industry (METI)’s “Research and Development of Quantum-Supercomputers Hybrid Platform for Exploration of Uncharted Computable Capabilities” (Project Leader: Mitsuhisa Sato) as part of the “Project for Research and Development of Enhanced Infrastructures for Post 5G Information and Communications Systems (JPNP20017).”