High-temperature Gibbs states with arbitrary external fields admit O(log n) quantum mixing via a detailed-balance Lindbladian and exhibit classical sampling hardness for β < 1.
Ap- proximating Gibbs states of local Hamiltonians efficiently with PEPS
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Develops a translationally invariant tensor network algorithm to calculate disorder-averaged quantities in infinite random spin chains without sampling, benchmarked on the random transverse-field Ising model at its infinite-randomness critical point.
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Rapid mixing for high-temperature Gibbs states with arbitrary external fields
High-temperature Gibbs states with arbitrary external fields admit O(log n) quantum mixing via a detailed-balance Lindbladian and exhibit classical sampling hardness for β < 1.
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Extracting average properties of disordered spin chains with translationally invariant tensor networks
Develops a translationally invariant tensor network algorithm to calculate disorder-averaged quantities in infinite random spin chains without sampling, benchmarked on the random transverse-field Ising model at its infinite-randomness critical point.