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 E-Mail LOS ALAMOS, N.M., May 12, 2021 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 today 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.

Andrew sornborgerZoe holmesLos alamosAndreas albrechtPatrickj colesAndrewt sornborgerAndrew arrasmithOak ridge national laboratoryOffice of scienceDepartment of physicsLos alamos national laboratoryQuantum science center at los alamosUs department of energyPhysical reviewQuantum science centerOak ridge national