During their early attempts to simplify and generalize what MELVIN had found, Krenn and his colleagues realized that the solution resembled abstract mathematical forms called graphs, which contain vertices and edges and are used to depict pairwise relations between objects. For these quantum experiments, every path a photon takes is represented by a vertex. And a crystal, for example, is represented by an edge connecting two vertices. MELVIN first produced such a graph and then performed a mathematical operation on it. The operation, called "perfect matching," involves generating an equivalent graph in which each vertex is connected to only one edge. This process makes calculating the final quantum state much easier, although it is still hard for humans to understand.

That changed with MELVIN's successor THESEUS, which generates much simpler graphs by winnowing the first complex graph representing a solution that it finds down to the bare minimum number of edges and vertices (such that any further deletion destroys the setup's ability to generate the desired quantum states). Such graphs are simpler than MELVIN's perfect matching graphs, so it is even easier to make sense of any AI-generated solution.

Renner is particularly impressed by THESEUS's human-interpretable outputs. "The solution is designed in such a way that the number of connections in the graph is minimized," he says. "And that's naturally a solution we can better understand than if you had a very complex graph."

Eric Cavalcanti of Griffith University in Australia is both impressed by the work and circumspect about it. "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."

Steinberg agrees. "For now, they are just amazing tools," he says. "And like all the best tools, they're already enabling us to do some things we probably wouldn't have done without them."