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The Information Content of Krylov Observables: A Machine Learning Approach
disordered-systems-and-neural-networkshigh-energy-physics-theorymathematical-physicsquantum-physicsstatistical-mechanics
We employ machine learning techniques to estimate the amount of information contained in three Krylov-space observables: the spread complexity $C(t)$, the discrete Wigner negativity $N(t)$, and the normalized negativity $χ(t)=N(t)/|S(t)|$, where $S(t)$ is the survival amplitude. We study thermofield
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