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This algorithmic breakthrough accelerates tensor-network contraction by up to 100×, enabling complex simulations of quantum materials like Heisenberg antiferromagnets on a single H100 GPU. By replacing large matrix decompositions with smaller subspace iterations, the method shifts computational bottlenecks to operations that GPUs can process more efficiently. This advancement significantly expands the scope of accessible calculations in condensed matter physics without requiring specialized quantum hardware.
Subspace Iteration Cuts Tensor-Network Contraction Times by 100×

Yining Zhang & Philippe Corboz’s SI-CTMRG uses subspace iteration to replace large SVDs with smaller ones, accelerating tensor-network contraction by up to 100×.
A single H100 graphics processing unit completed a complex calculation for the triangular-lattice Heisenberg antiferromagnet in approximately 10 hours, thanks to a new algorithm developed by Yining…
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