Learning the black hole metric from holographic conductivity

Li, Kai; Ling, Yi; Liu, Peng; Wu, Meng-He

doi:10.1103/physrevd.107.066021

Cited by 6 publications

(1 citation statement)

References 43 publications

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“…Entanglement measures are by far not the only data with which one can reconstruct the bulk geometry; work in this direction include [30,[52][53][54][55][56]. Other quantities that have been investigated in the literature include operators [57,58], four-point correlators [59], differential entropy [60,61], fidelity [62], complexity [63], light-cone cuts [64,65], chiral condensate [66,67], conductivity [68], hadron spectra [69], and magnetization [70] with varying degree of assumptions about the dual classical bulk gravity. To all of these cases one could incorporate the Hamiltonian Monte Carlo approach discussed in Sec.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Holographic spacetime from lattice Yang-Mills theory

et al. 2022

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Entanglement entropy is a notoriously difficult quantity to compute in strongly interacting gauge theories. Existing lattice replica methods have suffered from a severe signal-to-noise ratio problem, making high-precision studies prohibitively expensive. Our improved lattice method mitigates this situation and allows us to probe holographic predictions for the behavior of entanglement entropies in three- and four-dimensional Yang-Mills theories. We use this data for the numerical reconstruction of holographic bulk metrics.

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Section: Discussion and Outlookmentioning

confidence: 99%

Holographic spacetime from lattice Yang-Mills theory

et al. 2022

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Deep learning bulk spacetime from boundary optical conductivity

Ahn,

Jeong,

Kim

et al. 2024

J. High Energ. Phys.

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We employ a deep learning method to deduce the bulk spacetime from boundary optical conductivity. We apply the neural ordinary differential equation technique, tailored for continuous functions such as the metric, to the typical class of holographic condensed matter models featuring broken translations: linear-axion models. We successfully extract the bulk metric from the boundary holographic optical conductivity. Furthermore, as an example for real material, we use experimental optical conductivity of UPd2Al3, a representative of heavy fermion metals in strongly correlated electron systems, and construct the corresponding bulk metric. To our knowledge, our work is the first illustration of deep learning bulk spacetime from boundary holographic or experimental conductivity data.

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Reconstructing black hole exteriors and interiors using entanglement and complexity

2023

J. High Energ. Phys.

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Based on the AdS/CFT correspondence, we study how to reconstruct bulk spacetime metrics by various quantum information measures on the boundary field theories, which include entanglement entropy, mutual information, entanglement of purification, and computational complexity according to the proposals of complexity=volume 2.0 and complexity=generalized volume. We present several reconstruction methods, all of which are free of UV divergence and most of which are driven by the derivatives of the measures with respect to the boundary scales. We illustrate that the exterior and interior of a black hole can be reconstructed using the measures of spatial entanglement and time-evolved complexity, respectively. We find that these measures always probe the spacetime in a local way: reconstructing the bulk metric in different radial positions requires the information at different boundary scales. We also show that the reconstruction method using complexity=volume 2.0 is the simplest and has a certain strong locality.

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Learning the black hole metric from holographic conductivity

Cited by 6 publications

References 43 publications

Holographic spacetime from lattice Yang-Mills theory

Holographic spacetime from lattice Yang-Mills theory

Deep learning bulk spacetime from boundary optical conductivity

Reconstructing black hole exteriors and interiors using entanglement and complexity

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