Generalized symmetries generate exponentially many Krylov sectors in quantum many-body systems, showing that Hilbert space fragmentation does not by itself imply ergodicity breaking.
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Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
The peak-valley mechanism organizes strong Hilbert space fragmentation in 1D spin chains by assigning emergent good quantum numbers to the heights and depths of peaks and valleys.
A disorder-free spin ladder model exhibits a reversed quantum disentangled liquid at strong rung coupling, where light spins thermalize and heavy spins localize, establishing a microscopic origin for quasi-MBL.
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Hilbert Space Fragmentation from Generalized Symmetries
Generalized symmetries generate exponentially many Krylov sectors in quantum many-body systems, showing that Hilbert space fragmentation does not by itself imply ergodicity breaking.
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Fragmentation is Efficiently Learnable by Quantum Neural Networks
Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
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Peak-valley mechanism for Hilbert space fragmentation
The peak-valley mechanism organizes strong Hilbert space fragmentation in 1D spin chains by assigning emergent good quantum numbers to the heights and depths of peaks and valleys.
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Crossover from Quantum Chaos to a Reversed Quantum Disentangled Liquid in a Disorder-Free Spin Ladder
A disorder-free spin ladder model exhibits a reversed quantum disentangled liquid at strong rung coupling, where light spins thermalize and heavy spins localize, establishing a microscopic origin for quasi-MBL.