Unsupervised PCA applied to classical shadow data from random Pauli measurements detects and classifies both symmetry-breaking and topological quantum phase transitions across multiple spin models without Hamiltonian knowledge.
Mendes-Santos, A
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2025 2verdicts
UNVERDICTED 2representative citing papers
Configuration-space geometry yields universal scaling √Var(r_H) ~ L^{-2β/ν} at criticality for zero-magnetization systems and enables information-geometric detection of phase transitions in TFIM and SSH models.
citing papers explorer
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A Unsupervised Framework for Identifying Diverse Quantum Phase Transitions Using Classical Shadow Tomography
Unsupervised PCA applied to classical shadow data from random Pauli measurements detects and classifies both symmetry-breaking and topological quantum phase transitions across multiple spin models without Hamiltonian knowledge.
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On the criticality of the configuration-space statistical geometry
Configuration-space geometry yields universal scaling √Var(r_H) ~ L^{-2β/ν} at criticality for zero-magnetization systems and enables information-geometric detection of phase transitions in TFIM and SSH models.