Pushing the Classical Frontier of 1D Fermi-Hubbard Quench Dynamics Beyond Current Quantum Simulations

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Pushing the Classical Frontier of 1D Fermi-Hubbard Quench Dynamics Beyond Current Quantum Simulations

Authors

Roman Rausch, Sukhbinder Singh, Saeed S. Jahromi, Augustine Kshetrimayum, Roman Orus

Abstract

Establishing quantum advantage requires comparison against the best achievable classical simulation. The Q-CTRL team recently simulated quench dynamics of the one-dimensional Fermi-Hubbard model on an IBM processor, completing a $L=60$ evolution to time $t=6$ in under three minutes and claiming a $3000\times$ speedup over classical Time-Dependent Variational Principle (TDVP) simulation at bond dimension $χ=4096$. Their classical benchmark required over 160 hours on a CPU cluster, failed to converge in the high-entanglement regime $t\in[5.2,6]$, and left the most challenging window of the experiment unverified. Here, we push the boundaries of classical simulation by exploiting the full $\mathrm{U}(1)\times\mathrm{SU}(2)$ symmetry of the Fermi-Hubbard Hamiltonian combined with GPU-accelerated tensor contractions. Reaching bond dimensions up to $χ\approx62{,}000$ on four NVIDIA H200 GPUs -- among the largest ever achieved in TDVP simulations and fifteen times larger than Q-CTRL's classical baseline -- we achieve fully converged results across the entire simulation window, including rigorous certification of the previously unresolved high-entanglement regime $t\in[5.2,6]$. We further advance the classical frontier to $t=7$, which lies beyond the quantum hardware experiment and any previously verified classical evolution of the full wavefunction. At the bond dimension comparable to Q-CTRL's best classical run, our GPU implementation completes in $\sim\!100$ minutes, directly reducing the claimed $3000\times$ quantum advantage to $\sim\!36\times$. These results substantially narrow the quantum-classical performance gap and establish a new standard for tensor-network benchmarking of large-scale quantum simulations.

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