IBM (NYSE:IBM) on March 12, 2026 released its first reference architecture designed for quantum-centric supercomputing, outlining how quantum processors can be integrated with classical computing technologies such as CPUs and GPUs across on-premises systems, research facilities and cloud platforms.
The framework links quantum hardware with traditional computing infrastructure, high-speed networking and shared data storage to handle complex computational tasks. IBM’s model coordinates workflows that combine quantum and classical resources using integrated orchestration tools and open-source software frameworks, including Qiskit.
“The future lies in quantum-centric supercomputing, where quantum processors work together with classical high-performance computing to solve problems that were previously out of reach,” said Jay Gambetta, Director of IBM Research and IBM Fellow.
Several collaborative research projects have already demonstrated the potential of the architecture. Scientists from IBM, the University of Manchester, Oxford University, ETH Zurich, EPFL and the University of Regensburg successfully created a half-Möbius molecule and verified its electronic structure using quantum-centric supercomputing, with their findings published in Science. Meanwhile, researchers at Cleveland Clinic simulated a 303-atom tryptophan-cage mini-protein, representing one of the largest molecular models processed on a quantum-centric supercomputer.
In another collaboration, researchers from IBM, RIKEN and the University of Chicago determined the lowest-energy state of engineered quantum systems, outperforming methods that rely solely on classical computing. Scientists from RIKEN and IBM also carried out quantum simulations of iron-sulfur clusters using a closed-loop data exchange between an IBM Quantum Heron processor and all 152,064 classical compute nodes of RIKEN’s Fugaku supercomputer.
IBM is also partnering with Rensselaer Polytechnic Institute to enhance workflow scheduling and orchestration between quantum and high-performance computing systems. The company said that new algorithms built on this architecture are expected to accelerate applications in fields such as chemistry, materials science and optimization.