A Monte Carlo sampling scheme evaluates Lehmann representations for quench dynamics in integrable models, applied to the order parameter evolution in the repulsive Lieb-Liniger gas across interaction strengths.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3roles
background 1polarities
background 1representative citing papers
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.
LC-inequivalent graph-state blocks in random Clifford circuits yield distinct entanglement velocities v_E and butterfly velocities v_B, correlated with internal entanglement distribution and graph connectivity.
citing papers explorer
-
Non-equilibrium quantum dynamics of interacting integrable models by Monte Carlo sampling Lehmann representations
A Monte Carlo sampling scheme evaluates Lehmann representations for quench dynamics in integrable models, applied to the order parameter evolution in the repulsive Lieb-Liniger gas across interaction strengths.
-
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.
-
Graph-State Circuit Blocks control Entanglement and Scrambling Velocities
LC-inequivalent graph-state blocks in random Clifford circuits yield distinct entanglement velocities v_E and butterfly velocities v_B, correlated with internal entanglement distribution and graph connectivity.