Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments
Koji Hashimoto, Kumihiro Matsuo, Minoru Murata, Gakuto Ogiwara, Daichi Takeda
Abstract
Abstract We introduce a novel interpretable neural network (NN) model designed to perform precision bulk reconstruction under the AdS/CFT correspondence. According to the correspondence, a specific condensed matter system on a ring is holographically equivalent to a gravitational system on a bulk disk, through which tabletop quantum gravity experiments may be possible as reported in (Hashimoto et al 2023 Phys. Rev. Res. 5 023168). The purpose of this paper is to reconstruct a higher-dimensional gravity metric from the condensed matter system data via machine learning using the NN. Our machine reads spatially and temporarily inhomogeneous linear response data of the condensed matter system, and incorporates a novel layer that implements the Runge–Kutta method to achieve better numerical control. We confirm that our machine can let a higher-dimensional gravity metric be automatically emergent as its interpretable weights, using a linear response of the condensed matter system as data, through supervised machine learning. The developed method could serve as a foundation for generic bulk reconstruction, i.e. a practical solution to the AdS/CFT correspondence, and would be implemented in future tabletop quantum gravity experiments.