Quantum Machine Learning Hits a Limit: A Black Hole Permanently Scrambles Information That Can't Be Recovered

A new theorem shows that information run through an information scrambler such as a black hole will reach a point where any algorithm will be unable to learn the information that has been scrambled. A black hole permanently scrambles information that can't be recovered with any quantum machine learning algorithm, shedding new light on the classic Hayden-Preskill thought experiment. A new theorem from the field of quantum machine learning has poked a major hole in the accepted understanding about information scrambling. "Our theorem implies that we are not going to be able to use quantum machine learning to learn typical random or chaotic processes, such as black holes. In this sense, it places a fundamental limit on the learnability of unknown processes," said Zoe Holmes, a post-doc at Los Alamos National Laboratory and coauthor of the paper describing the work published on May 12, 2021, in Physical Review Letters. "Thankfully, because most physically interesting processes are sufficiently simple or structured so that they do not resemble a random process, the results don't condemn quantum machine learning, but rather highlight the importance of understanding its limits," Holmes said.