A communication-aware multi-GPU distribution approach for tensor network contraction reports 7-173x extra speedup over slicing on 8 H100 GPUs and 42x to 67,869x on 1024 GPUs.
Tensor networks and quantum error correction
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
A distributed (6.6.6) color code is realized by interconnecting patches via entangled pairs, with simulations showing the concatenated MWPM decoder maintains error threshold under asymmetric seam noise while tensor-network decoder shows slight reduction.
A topical review unifying statistical mechanics, tensor network, and AI approaches to approximate maximum likelihood decoding for quantum error correction codes.
citing papers explorer
-
Distributed Realization of Color Codes for Quantum Error Correction
A distributed (6.6.6) color code is realized by interconnecting patches via entangled pairs, with simulations showing the concatenated MWPM decoder maintains error threshold under asymmetric seam noise while tensor-network decoder shows slight reduction.