Quantum Simulation, High Performance Computing

NVIDIA H200 GPUs Enable Largest TDVP Simulations of Real-Time Dynamics

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June 9, 2026 by Ivy Delaney

Reaching bond dimensions of approximately 62,000, researchers at Multiverse Computing have achieved some of the largest reported TDVP simulations of real-time quantum dynamics, utilizing up to four NVIDIA H200 GPUs. This advance enabled fully converged simulations of the one-dimensional Fermi-Hubbard model, exceeding the limits of previous classical approaches and providing a rigorous classical certification of the high-entanglement regime observed in a recent 120-qubit quantum experiment. The team extended simulations to t = 7, a timeframe longer than that attained by the quantum processor used for validation. These results demonstrate how algorithmic improvements and modern GPU architectures are expanding the boundaries of classical simulation, and suggest a need to carefully re-evaluate claims of quantum advantage in this specific area of physics.

Researchers developed a framework combining U(1)×SU(2) symmetry-preserving tensor networks with advanced algorithms to address complex quantum simulations, surpassing previous classical limitations in the one-dimensional Fermi-Hubbard model. Multiverse Computing’s advancements in tensor-network algorithms and high-performance computing are demonstrably pushing the boundaries of classical simulation, offering important new benchmarks for the quantum computing field and suggesting a need for careful comparison against the most powerful classical methods available.

TDVP Simulations Validate 120-Qubit Quantum Experiment to t = 7

Researchers extended simulations of the one-dimensional Fermi-Hubbard model to t = 7, surpassing the timeframe attained by the 120-qubit quantum processor experiment under validation. The Multiverse team’s success stems from a combination of U(1)×SU(2) symmetry-preserving tensor networks, GPU acceleration, and advanced algorithms, pushing the boundaries of classical simulation capabilities. These results demonstrate that ongoing improvements in computational methods and hardware continue to challenge the perceived dominance of quantum computing, establishing new benchmarks for the field and demanding continuous reassessment of quantum advantage claims.

They also highlight the importance of continuously reassessing quantum advantage claims against the strongest available classical methods.

Multiverse Computing
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Fermi-Hubbard Model GPU Multiverse Computing Quantum simulation Tensor Networks
Avatar of Ivy Delaney

Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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