Google is starting up a new Quantum Artificial Intelligence Lab to find ways to make computers much smarter so they can help solve some of the world's most challenging problems, from diseases to environmental threats.
The idea, according to a May 16 post by Hartmut Neven, director of engineering for the Google Research team on the Google Research Blog, is to assemble the lab at NASA's Ames Research Center using a quantum computer from D-Wave Systems, then to have the Universities Space Research Association (USRA) invite researchers from around the world to use it for advanced studies. "Our goal: to study how quantum computing might advance machine learning," Neven wrote.
The possibilities are almost endless, according to Google. "We believe quantum computing may help solve some of the most challenging computer science problems, particularly in machine learning," wrote Neven. "Machine learning is all about building better models of the world to make more accurate predictions. If we want to cure diseases, we need better models of how they develop. If we want to create effective environmental policies, we need better models of what's happening to our climate. And if we want to build a more useful search engine, we need to better understand spoken questions and what's on the web so you get the best answer."
That's why advances in today's computers are needed. "Machine learning is highly difficult … because building a good model is really a creative act," wrote Neven. "As an analogy, consider what it takes to architect a house. You're balancing lots of constraints—budget, usage requirements, space limitations, etc.—but still trying to create the most beautiful house you can. A creative architect will find a great solution. Mathematically speaking the architect is solving an optimization problem and creativity can be thought of as the ability to come up with a good solution given an objective and constraints."
But typical computers can't easily do those kinds of creative things, he wrote. "That's where quantum computing comes in. It lets you cheat a little," giving researchers the ability to "see" creative possibilities that traditional computers can't find.
"We've already developed some quantum machine learning algorithms," wrote Neven. "One produces very compact, efficient recognizers—very useful when you're short on power, as on a mobile device. Another can handle highly polluted training data, where a high percentage of the examples are mislabeled, as they often are in the real world. And we've learned some useful principles: e.g., you get the best results not with pure quantum computing, but by mixing quantum and classical computing."
Some of the potential projects for the lab include things like constructing more efficient and more accurate models for everything from speech recognition to Web search to protein folding, wrote Neven. "We actually think quantum machine learning may provide the most creative problem-solving process under the known laws of physics."