Scientists at Cleveland Clinic, RIKEN, and IBM have successfully modeled a 12,635-atom protein, trypsin, the largest biologically meaningful molecule ever simulated using quantum computers. This achievement represents a substantial leap from previous quantum simulations limited to just 10 atoms. The breakthrough was enabled by a novel framework called quantum-centric supercomputing, which combines the power of quantum and classical computers to tackle complex molecular systems relevant to biology and chemistry. Researchers rapidly scaled their modeling capabilities, achieving simulations roughly 40 times larger than those possible six months prior, with key workflow accuracy improving by up to 210 percent. “For years, quantum computing has been a promise. Now, quantum computers are producing results that matter to science,” said Jay Gambetta, Director of IBM Research and IBM Fellow, highlighting the technology’s maturation into a useful scientific tool with potential applications in drug discovery.
12,635-Atom Protein Simulation Achieved via Quantum Computing
The successful simulation of a 12,635-atom protein, trypsin, marks a pivotal moment in quantum computing’s evolution from theoretical promise to practical scientific application. This achievement represents the largest biologically relevant molecule ever modeled using quantum hardware. This leap forward, detailed in a recent pre-print study, dramatically expands the scope of molecular simulations possible and signals a shift in how quantum computers are evaluated, focusing on the complexity of the problems they can address rather than solely qubit count. This approach wasn’t simply about brute-force quantum processing power; instead, classical computers first deconstructed the protein-ligand complexes into manageable fragments. IBM’s 156-qubit IBM Quantum Heron processors, located at both Cleveland Clinic and RIKEN, then calculated the quantum-mechanical behavior of those pieces. These calculations were paired with the processing power of Fugaku and Miyabi-G supercomputers to reassemble a complete molecular representation.
The team’s innovative algorithm, dubbed EWF-TrimSQD, was critical, dramatically reducing computational overhead and accelerating the ability to represent molecular chemistry on quantum hardware. “By crossing the 12,000-atom barrier, we have significantly expanded the scale of biologically meaningful molecular simulations possible with quantum computing and demonstrated a framework for applying these methods to scientifically relevant problems at a larger scale,” said Kenneth Merz, Ph.D., lead author of the study and staff scientist in Cleveland Clinic’s Computational Life Sciences Department. This research was motivated by the significant challenges in predicting how drug candidates bind to proteins, a process that currently consumes considerable time and resources in pharmaceutical development. This advancement could ultimately shorten drug development timelines, potentially reducing the decade-long process and substantial investment currently required to bring a single medicine to market. The team intends to build on this work, aiming to simulate even larger and more complex molecular systems to further refine drug discovery processes and unlock new insights into biological mechanisms.
Quantum-Centric Supercomputing Enables Molecular Modeling Scale-Up
The pursuit of simulating complex molecular interactions has long been hampered by computational limitations, but a novel approach combining quantum and classical computing is now yielding results. While early quantum simulations were restricted to simple systems of around ten atoms, scientists are now routinely modeling molecules of biological significance, opening new avenues for research in fields like drug discovery and materials science. This isn’t simply a matter of increasing processing power; it’s about strategically allocating computational tasks to the tools best suited for the job. A collaborative effort between Cleveland Clinic, RIKEN, and IBM recently achieved a landmark simulation of the trypsin protein, a biologically relevant molecule comprising 12,635 atoms, the largest such simulation ever completed using quantum computers. This leap forward was facilitated by a framework known as quantum-centric supercomputing, which leverages the strengths of both quantum processors and classical supercomputers.
The team’s success isn’t solely attributable to hardware; algorithmic innovation played a crucial role. The development of EWF-TrimSQD, a novel quantum-classical hybrid algorithm, dramatically reduced computational overhead and accelerated the simulation process. This allowed for a 210 percent improvement in the accuracy of key simulation steps within just six months, and a roughly 40-fold increase in the size of the modeled proteins. IBM’s commitment to this hybrid approach is evident in the installation of a quantum computer directly at the Cleveland Clinic in Ohio, signaling a move toward on-site quantum computing resources for medical research. The ability to accurately model molecular interactions at this scale promises to accelerate drug development by providing a more precise understanding of how potential drug candidates bind to protein targets, potentially shortening timelines that currently span a decade or more.
This work marks an important advance and underscores quantum computing’s emerging role on systems of relevance to drug discovery.
Kenneth Merz, Ph.D., lead author of the study and staff scientist in Cleveland Clinic’s Computational Life Sciences Department
EWF-TrimSQD Algorithm Improves Simulation Accuracy 210x
Kenneth Merz of Cleveland Clinic’s Computational Life Sciences Department and his collaborators have achieved a significant leap in molecular simulation, successfully modeling the protein trypsin, a complex structure of 12,635 atoms, using a novel quantum-centric supercomputing approach. This achievement surpasses previous quantum simulations, which were largely limited to systems of around 10 atoms, and demonstrates a growing capacity to tackle biologically relevant molecular structures. The team’s work isn’t solely about increasing computational scale; it’s about refining the methodology to efficiently harness the strengths of both quantum and classical computing resources. This innovation allowed for a 210x improvement in simulation accuracy within a key workflow step, a substantial gain achieved over just six months of development. Up to 94 qubits were utilized, performing nearly 6,000 quantum operations during certain phases of the simulation, highlighting the intensive computational demands of the process.
Drug Discovery Potential Fueled by Larger Molecular Simulations
The ability to accurately simulate complex molecular interactions is rapidly becoming a cornerstone of modern drug discovery, and a recent collaboration between Cleveland Clinic, RIKEN, and IBM has dramatically expanded the scale of these simulations. This leap in scale isn’t merely about computational power, but a strategic integration of different computing architectures. Results are then reassembled by classical supercomputers, Fugaku at RIKEN and Miyabi-G at the University of Tokyo and the University of Tsukuba, to create a complete molecular representation. This progress addresses a significant bottleneck in drug development: accurately predicting how a potential drug candidate will bind to a protein target, a process that currently consumes substantial time and investment. The ability to accurately model these interactions earlier in the discovery process could substantially shorten the decade-long timelines often associated with bringing a new medicine to market.
