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Status: Unchecked ×Keyword: tensor contraction ×Clear all

2 claims from 1 paper

1. Physics and Astronomy › Quantum many-body systems

   Neural tensor contractions and the expressive power of deep neural quantum states

   Sharir, Shashua and Carleo · Physical review. B./Physical review. B · 2022

   The paper links tensor networks to deep feed-forward neural networks by building layers that perform tensor contractions, and draws conclusions about how expressive neural quantum states are.

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   1. UncheckedNeural-network quantum states can represent anything practically usable variational tensor networks can, and possibly more, according to the paper.“In the context of many-body quantum states, this result establishes that neural-network states have strictly the same or higher expressive power than practically usable variational tensor networks.”
   2. UncheckedThe paper says some quantum states cannot be efficiently written as matrix product states or PEPS but can be efficiently written as neural network states.“The opposite instead does not hold true, and our results imply that there exist quantum states that are not efficiently expressible in terms of matrix product states or PEPS, but that are instead efficiently expressible with neural network states.”