A technique extracts k-local conserved operators from iPEPS by identifying vanishing fidelity susceptibility in a quantum geometry of parameter-deformed states, yielding improved parent Hamiltonians for RVB and deformed toric code states.
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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.
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Extracting conserved operators from a projected entangled pair state
A technique extracts k-local conserved operators from iPEPS by identifying vanishing fidelity susceptibility in a quantum geometry of parameter-deformed states, yielding improved parent Hamiltonians for RVB and deformed toric code states.
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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.