Cleveland Clinic, RIKEN and IBM Named Finalists for 2026 ACM Gordon Bell Prize for Quantum Simulation of a 12,635-Atom Protein
核心洞察
A multidisciplinary team from Cleveland Clinic, RIKEN (搜索) and IBM advanced to the finals of the 2026 ACM Gordon Bell Prize for simulating the largest biologically meaningful molecules ever modeled with quantum computers, reaching 12,635 atoms.
The work unites quantum and classical computing in a quantum-centric supercomputing framework, using up to 94 qubits and nearly 6,000 quantum operations alongside the Fugaku and Miyabi-G supercomputers.
The team scaled its method roughly 40-fold and achieved a 210-fold improvement in accuracy within a year, while improving computed binding energies relevant to predicting drug–protein interactions.
A multidisciplinary team from Cleveland Clinic, RIKEN (搜索) and IBM has been named 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. The achievement was made possible by uniting 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.
Rooted in Drug Discovery
The origins of the project lie in the team's investigation into how computation in drug discovery can be improved. That effort rests on two fundamental challenges: first, modeling the movement of atoms as biological processes unfold; and second, accurately computing their energies. The second challenge is particularly 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 IBM Quantum Heron processors running within IBM quantum computers at Cleveland Clinic in the United States and RIKEN (搜索) in Japan. The calculations were executed alongside two of the world's most powerful supercomputers, Fugaku at RIKEN and Miyabi-G, operated by the University of Tokyo and University of Tsukuba.
Technical Approach and Scale
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 then used to reassemble the results and allow the team to gain a complete representation of each molecule.
Using sample-based quantum diagonalization by IBM and RIKEN (搜索)—featured on the cover of Science Advances—along with embedded wavefunction methods adapted by Cleveland Clinic, the team reported the first-known simulation of a 303-atom protein achieved with quantum computers. Less than a year later, the team scaled its method roughly 40 times while also achieving a 210 times improvement in accuracy.
Improving Binding-Energy Accuracy
In updated results published in September, the team advanced the work further. Most notably, they further improved 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.
The team also validated the workflow on a third supercomputer, the JHPC-quantum GPU supercomputer "ROQUO," RIKEN (搜索)'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.
Implications for Drug Discovery
The quantum-classical techniques developed by the team continue to reduce the computational overhead required to directly represent the chemistry of molecular systems with accuracy, 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 systems can be calculated and represents a step toward helping researchers better predict how medicines may interact with protein targets.
The research team includes Kenneth Merz Jr, Akhil Shajan, Danil Kaliakin and Fangchun Liang of Cleveland Clinic; Yuichi Otsuka, Tomonori Shirakawa, Lukas Broers, Han Xu, Miwako Tsuji, Mitsuhisa Sato and Seiji Yunoki of RIKEN (搜索) 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 and Mario Motta of IBM.
The 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), as part of the "Project for Research and Development of Enhanced Infrastructures for Post 5G Information and Communications Systems (JPNP20017)."