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Status: Unchecked ×Keyword: gapped Hamiltonians ×Clear all

2 claims from 1 paper

1. Physics and Astronomy › Quantum many-body systems

   Provably efficient machine learning for quantum many-body problems

   Huang, Kueng, Torlai, Albert and Preskill · Science · 2022

   The paper proves that classical machine learning can efficiently predict ground state properties of gapped quantum systems and classify quantum phases, supported by numerical experiments on several model systems.

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   1. UncheckedUnder widely accepted complexity assumptions, classical algorithms that do not learn from data cannot match the guarantee the paper proves for classical machine learning.“In contrast, under widely accepted complexity theory assumptions, classical algorithms that do not learn from data cannot achieve the same guarantee.”
   2. Unchecked“In this work, we prove that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltonians in finite spatial dimensions, after learning from data obtained by measuring other Hamiltonians in the same quantum phase of matter.”