Scientific American Reveals ‘Shocking’ Quantum Efforts from Machine Learning System
It was bound to happen. The intersection of AI and quantum physics has officially enabled the development of quantum solutions to problems through experiments designed by AI. Scientists are marveling over the results.
Quantum physicist Mario Krenn designed MELVIN, “a machine-learning algorithm” that learned how to mix and match the building blocks of standard quantum experiments and find solutions to new problems. It created a new experiment that led to unexpected results.
“When we understood what was going on, we were immediately able to generalize [the solution],” says Krenn, who is now at the University of Toronto. 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. Meanwhile Krenn, working with colleagues in Toronto, has refined their machine-learning algorithms. 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. 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.”
While some unique “thinking” was involved in devising the experiment, MELVIN figured out how to use an older process and update it for the experiment, which was still an impressive leap of learning, according to Eric Cavalcanti of Griffith University in Australia.
“These machine-learning techniques represent an interesting development. For a human scientist looking at the data and interpreting it, some of the solutions may look like ‘creative’ new solutions. But at this stage, these algorithms are still far from a level where it could be said that they are having truly new ideas or coming up with new concepts,” he says. “On the other hand, I do think that one day they will get there. So these are baby steps—but we have to start somewhere.”
read more at scientificamerican.com