The Python's Lunch: geometric obstructions to decoding Hawking radiation

TL;DR

This paper links the computational difficulty of extracting information from Hawking radiation to a geometric obstruction in the wormhole, called the 'Python's Lunch', based on tensor network models and entropy differences.

Contribution

It introduces a geometric interpretation of the computational hardness in black hole information retrieval, relating it to the shape of the Einstein-Rosen bridge and entropy differences.

Findings

Identifies the 'Python's Lunch' as a geometric obstruction to decoding Hawking radiation.

Proposes a formula relating computational hardness to entropy differences of quantum extremal surfaces.

Connects tensor network models to black hole geometry and information theory.

Abstract

According to Harlow and Hayden [arXiv:1301.4504] the task of distilling information out of Hawking radiation appears to be computationally hard despite the fact that the quantum state of the black hole and its radiation is relatively un-complex. We trace this computational difficulty to a geometric obstruction in the Einstein-Rosen bridge connecting the black hole and its radiation. Inspired by tensor network models, we conjecture a precise formula relating the computational hardness of distilling information to geometric properties of the wormhole - specifically to the exponential of the difference in generalized entropies between the two non-minimal quantum extremal surfaces that constitute the obstruction. Due to its shape, we call this obstruction the "Python's Lunch", in analogy to the reptile's postprandial bulge.