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Machine learning spatial geometry from entanglement features
Physical review B
Motivated by the close relations of the renormalization group with both the holography duality and the deep learning, we propose that the holographic geometry can emerge from deep learning the entanglement feature of a quantum many-body state. We develop a concrete algorithm, call the entanglement feature learning (EFL), based on the random tensor network (RTN) model for the tensor network holography. We show that each RTN can be mapped to a Boltzmann machine, trained by the entanglementdoi:10.1103/physrevb.97.045153 fatcat:54dkvdov6bffbl642ovz63bs5i