Work

Retrieving information from a black hole using quantum machine learning

Year: 2022

Type: article

Abstract: Information can be retrieved from a black hole that is sufficiently scrambling, assuming that the retriever has perfect control of the emitted Hawking radiation and perfect knowledge of the internal dynamics of the black hole. In this paper, the authors show that for black holes modeled by a certain class of random quantum circuits, information can be retrieved by using quantum machine learning to analyze out-of-time-order correlation functions.

Source: Physical Review A

DOI: 10.1103/physreva.106.062434

Authors: Lorenzo Leone, Salvatore F. E. Oliviero, Stefano Piemontese, Sarah True, Alioscia Hamma

Institutions: University of Massachusetts Boston, University of Naples Federico II

Language: English

Topic: Black Holes and Theoretical Physics

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Award: Transitions in Quantum Complexity