Machine learning is highly difficult. Classical computers aren?t well suited to these types of creative problems. That?s where quantum computing comes in. It lets you cheat a little, giving you some chance to ?tunnel? through a ridge to see if there?s a lower valley hidden beyond it. This gives you a much better shot at finding the true lowest point -- the optimal solution.
We?ve already developed some quantum machine learning algorithms. 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.
