Kavli Affiliate: Leon Balents
| First 5 Authors: Tarun Advaith Kumar, Tarun Advaith Kumar, , ,
| Summary:
A variety of generative neural networks recently adopted from machine
learning have provided promising strategies for studying quantum matter. In
particular, the success of autoregressive models in natural language processing
has motivated their use as variational ans"atze, with the hope that their
demonstrated ability to scale will transfer to simulations of quantum many-body
systems. In this paper, we introduce an autoregressive framework to calculate
finite-temperature properties of a quantum system based on the imaginary-time
evolution of an ensemble of pure states. We find that established approaches
based on minimally entangled typical thermal states (METTS) have numerical
instabilities when an autoregressive recurrent neural network is used as the
variational ans"atz. We show that these instabilities can be mitigated by
evolving the initial ensemble states with a unitary operation, along with
applying a threshold to curb runaway evolution of ensemble members. By
comparing our algorithm to exact results for the spin 1/2 quantum XY chain, we
demonstrate that autoregressive typical thermal states are capable of
accurately calculating thermal observables.
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