![]() While it would take Krenn and his colleagues days or even weeks to understand MELVIN’s meanderings, they can almost immediately figure out what THESEUS is saying. Their latest effort, an AI called THESEUS, has upped the ante: it is orders of magnitude faster than MELVIN, and humans can readily parse its output. ![]() Meanwhile Krenn, working with colleagues in Toronto, has refined their machine-learning algorithms. Since then, other teams have started performing the experiments identified by MELVIN, allowing them to test the conceptual underpinnings of quantum mechanics in new ways. ![]() “When we understood what was going on, we were immediately able to generalize ,” says Krenn, who is now at the University of Toronto. MELVIN had cracked a far more complex puzzle. But those experiments had been much simpler. Eventually Krenn realized that the algorithm had rediscovered a type of experimental arrangement that had been devised in the early 1990s. Krenn, Anton Zeilinger of the University of Vienna and their colleagues had not explicitly provided MELVIN the rules needed to generate such complex states, yet it had found a way. MELVIN had seemingly solved the problem of creating highly complex entangled states involving multiple photons (entangled states being those that once made Albert Einstein invoke the specter of “spooky action at a distance”). “The first thing I thought was, ‘My program has a bug because the solution cannot exist,’” Krenn says. Its job was to mix and match the building blocks of standard quantum experiments and find solutions to new problems. MELVIN was a machine-learning algorithm Krenn had built, a kind of artificial intelligence. Quantum physicist Mario Krenn remembers sitting in a café in Vienna in early 2016, poring over computer printouts, trying to make sense of what MELVIN had found.
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