Symmetric superpositions of antipodal triplet states produce exact zero-energy eigenstates in non-integrable spin Hamiltonians that are scars with tunable entanglement from volume to area law.
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Machine learning reconstruction accuracy is substantially higher for spectral-edge eigenstates than for mid-spectrum eigenstates, providing a new quantitative measure of information content in many-body quantum states.
Higher moments of the projected process ensemble reveal entanglement structures that distinguish chaotic from integrable dynamics more sharply than quantum dynamical or spatiotemporal entropies.
citing papers explorer
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Symmetric tensor scars with tunable entanglement from volume to area law
Symmetric superpositions of antipodal triplet states produce exact zero-energy eigenstates in non-integrable spin Hamiltonians that are scars with tunable entanglement from volume to area law.
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Information in Many-body Eigenstates: A Question of Learnability
Machine learning reconstruction accuracy is substantially higher for spectral-edge eigenstates than for mid-spectrum eigenstates, providing a new quantitative measure of information content in many-body quantum states.
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Diagnosing chaos with projected ensembles of process tensors
Higher moments of the projected process ensemble reveal entanglement structures that distinguish chaotic from integrable dynamics more sharply than quantum dynamical or spatiotemporal entropies.