Alex King is director of the Critical Materials Institute, a part of the U.S. Department of Energy's Ames Laboratory. CMI is heavily involved in making rare earth minerals slightly less rare by means of supercomputer analysis; researchers there are approaching the ongoing crunch by looking both for substitute materials for things like gallium, indium, and tantalum, and easier ways of separating out the individual rare earths (a difficult process). One team there is working with "ligands – molecules that attach with a specific rare-earth – that allow metallurgists to extract elements with minimal contamination from surrounding minerals" to simplify the extraction process. We'll be talking with King soon; what questions would you like to see posed? (This 18-minute TED talk from King is worth watching first, as is this Q&A.)
Now, a Chinese team has successfully implemented this artificial intelligence algorithm on a working quantum computer, for the first time. The information processor is a standard nuclear magnetic resonance quantum computer capable of handling 4 qubits. The team trained it to recognize the difference between the characters '6' and '9' and then asked it to classify a set of handwritten 6s and 9s accordingly, which it did successfully. The team says this is the first time that this kind of artificial intelligence has ever been demonstrated on a quantum computer and opens the way to the more rapid processing of other big data sets — provided, of course, that physicists can build more powerful quantum computers.
"We were nervous about going down this path," says Jeremy Hilton, vice president of processor development at D-Wave Systems. "This architecture requires the qubits and the quantum devices to be intermingled with all these big classical objects. The threat you worry about is noise and impact of all this stuff hanging around the qubits. Traditional experiments in quantum computing have qubits in almost perfect isolation. But if you want quantum computing to be scalable, it will have to be immersed in a sea of computing complexity.