Quantum-Classical Workflow Simulates Largest Protein on Quantum Hardware

A collaborative team from Cleveland Clinic, RIKEN, and IBM has used a hybrid quantum-classical approach to simulate a biologically relevant molecule at a scale not previously reached with quantum hardware.

The simulation focused on trypsin, a protein with 12,635 atoms, including its binding partner and surrounding water molecules, representing the largest molecule of its kind modeled using quantum computing resources to date. The workflow combined quantum processors with traditional high-performance computing systems to carry out the calculations.

To achieve this, the researchers utilized up to 94 qubits on two IBM quantum computers and incorporated the Fugaku supercomputer at RIKEN and the Miyabi-G supercomputer at the University of Tokyo and University of Tsukuba. These classical systems handled subspace diagonalization tasks, while the quantum hardware processed other parts of the workflow.

The research team also simulated another protein-ligand pair, T4-Lysozyme, which consists of 11,608 atoms. Both simulations included the proteins in solution, reflecting conditions relevant to biological systems.

What the numbers show

• Trypsin simulation involved 12,635 atoms, the largest on quantum hardware
• Up to 94 qubits were used across two IBM quantum computers
• Approximately 9,200 quantum circuits ran over more than 100 hours
• About 1.3 billion measurement outcomes were collected
• The workflow achieved a 40-fold increase in system size and up to 210-fold improvement in accuracy over simulations from six months prior

The hybrid quantum-classical workflow ran for more than 100 hours, executing roughly 9,200 quantum circuits and gathering around 1.3 billion measurement results. This approach allowed the researchers to reach a scale and accuracy not previously reported in quantum chemistry simulations.

According to the published preprint, the simulation was completed and reported on May 5, 2026. The work was made possible by integrating quantum processors with classical computing resources, a method described as quantum-centric supercomputing.

This quantum-centric architecture is designed to coordinate tasks between quantum processors, GPUs, and CPUs, enabling scientific computations to shift between different types of hardware based on the requirements of each step. The approach aims to optimize workflows for complex scientific problems by leveraging the strengths of both quantum and classical systems.

Earlier in 2026, RIKEN and IBM demonstrated a similar quantum-classical workflow using the Fugaku supercomputer and an IBM Quantum Heron processor. That effort addressed a quantum chemistry problem at a scale and accuracy not previously achieved, laying the groundwork for the larger protein simulations reported later in the year.

* This article is based on publicly available information at the time of writing.