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Two Hungarian researchers, Andor Menczer and Örs Legeza Menczer, have set a new computational benchmark in the field of supercomputer simulation of complex quantum-physical systems. Their result is a milestone in the field of computer modelling of quantum matter, the HUN-REN Hungarian Research Network announced.

According to their release, the simulation program can solve 250 quadrillion elementary operations per second. This can help to reduce the cost of research to improve the efficiency of, for instance, drug development or energy transport.

Using the tensor network algorithm, ELTE PhD student Andor Menczer and HUN-REN Wigner Physics Research Center (PRC) scientific advisor Örs Legeza reached nearly a quarter of a PetaFlop on a single computer. Their findings were recently published in collaboration with the US Pacific Northwest National Laboratory, and startup industry partners NVIDIA and SandboxAQ.

“This achievement with AI accelerators sets a new standard in computational quantum matter modeling, challenging the performance balance between classical and quantum computers,” said Legeza.

The team reached 246 TeraFlops on the NVIDIA DGX-H100, comparable to the combined power of 80 high-performance, 128-core computers, or 700-1000 modern laptops. This performance is about half of the 0.6 PetaFlops capacity of Komondor, a Hungarian AI-enabled supercomputer.

This breakthrough highlights the potential of new hardware tools for algorithms beyond those solely based on AI. A joint press release on this accomplishment was issued by the US Department of Energy, Pacific Northwest National Laboratory, NVIDIA, and SandboxAQ.

Performance could be further boosted by linking multiple computers together, HUN-REN points out. This way, achieving a range of several PetaFlops with the so-called multinode setup is not an obstacle anymore. For context, in 2015, one of the world’s leading Japanese supercomputers reached a performance of 10 PetaFlops, they recalled. “The synergy of advanced mathematical algorithms and rapid IT advances is making it possible to study complex quantum systems that researchers once only dreamed of,” noted Legeza.

Beyond computational breakthroughs, the research has also achieved unprecedented performance in efforts to model complex metal-containing molecules.

Metal-containing catalysts play a crucial role in many industrial and biological processes, driving essential reactions, according to the release. These “powerhouses” of energy conversion are fundamental in industries spanning from medicine to energy generation. By accelerating chemical reactions, catalysts make processes more efficient and sustainable. “Optimizing these reactions is vital for tackling today’s global challenges, from green energy production to environmental sustainability,” Legeza explained.

The new simulation approach is generating industrial interest, as the combination of the tensor network algorithm with AI-based methods creates a revolutionary environment for the pharmaceutical and chemical sectors.

The substantial performance gains allow calculations that once took months to be completed in a day, providing a transformative toolkit for quantum chemical modeling. In collaboration with engineers at NVIDIA and AMD, the team continues to optimize the algorithm on newer hardware, HUN-REN concluded.

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