The human brain can still outperform our best algorithms for a variety of tasks. Some tasks, like object identification, aren't really surprising—our brain itself has been optimized through evolution to be pretty good at this. But there are other classes of problems that are a bit of a surprise, like some forms of optimization.

You might expect a computer to be pretty good at finding optimal solutions. But when it came to figuring out the optimal structure of a protein, people playing the game FoldIt managed to beat some of our best software. Now you can add a second task where our brains come out ahead: figuring out the best way to perform some quantum manipulations. All it took was turning quantum mechanics into a game.

Algorithms often come up short in optimization problems because of how they're structured. It's easiest to think of this idea as a landscape with peaks and valleys. The algorithm simply starts off by picking a large number of random locations within this landscape and then tries to move uphill from each of these locations. Once it finds a collection of peaks, it can compare them to find the highest peak that it has located, which can represent the optimal solution.

Unfortunately, it's also possible that all of the starting points lead to what are called local maxima—nearby high points that aren't actually the highest in the landscape.

This is where humans can excel. Rather than getting stuck in a local maximum, they can intuit that there might be a better solution available and use some trial-and-error to figure out ways of getting there. This is precisely what happened with FoldIt, where people managed to figure out that jiggling specific protein structures could get them to realign into a more energetically favorable state.

But protein structures are inherently visual and somewhat intuitive. Quantum mechanics is generally anything but. So how did the new project succeed?

The authors were working on a specific type of problem that may help with the development of quantum computers. Their system involves an array of atoms held in an optical lattice. It's possible to trap hundreds of atoms in these systems, which could allow these systems to scale well. But to perform operations, you have to physically shove atoms around so that they interact with each other. (Researchers use a form of light called optical tweezers to do this.)

Move the atoms too slowly and the system loses the quantum state you need before the interaction can take place. But move it too quickly and you also disrupt the system. "There is a shortest process duration with perfect fidelity, denoted the quantum speed limit (QSL)," the authors write, "which imposes a fundamental limit on the process duration." So figuring out the best way to approach the speed limit is a significant challenge—and an optimization challenge.

It was also a challenge to turn it into a game. But the authors, from Denmark's Aarhus University, visualized it as liquid gathered at a low point of a flexible line. Your job as a player was to flex the line in such a way that as much of the liquid as possible would pool at a target location. Move it too slowly and your score would suffer. Move it too quickly and it would slosh all over the place, also lowering your score. They called the game "Bring Home Water" and turned it into an application called Quantum Moves, which is available for download (it's also on the App Store and Google Play).

When you grab and flex the surface, you're actually doing the equivalent of moving the optical tweezers. In fact, the comparison is so direct that the results could tell the researchers where to locate the tweezers relative to the atom in order to perform the equivalent manipulations. The liquid is just the quantum state of the atom, and moving it too quickly introduces excitations in its wave function.

Once again, some of the players beat our best algorithms, as they were willing to sacrifice a bit of stability in order to get a good time. Overall, they found solutions similar to those of the algorithms in far fewer tries.

So the authors came up with a hybrid solution. Rather than seeding their optimization software with random starting points, they seeded it with some of the best solutions described by the readers. The resulting analysis produced a value for the quantum speed limit that was less than 70 percent of the value produced by the algorithms alone. In other words, it shaved more than 30 percent off the time needed.

The authors were so impressed by the results that they already have another game available on their website, and they plan on extending the approach to a number of other problems.

Nature, 2015. DOI: 10.1038/nature17620 (About DOIs).