Machine Learning Could be Revolutionized by Quantum Computing

(TechnologyReview) Certain machine-learning tasks could be revolutionized by more powerful quantum computers according to a new study conducted by researchers from IBM and MIT. They showed how an IBM quantum computer can accelerate a specific type of machine-learning task called feature matching. The team says that future quantum computers should allow machine learning to hit new levels of complexity.
Feature matching is a technique that converts data into a mathematical representation that lends itself to machine-learning analysis. The resulting machine learning depends on the efficiency and quality of this process. Using a quantum computer, it should be possible to perform this on a scale that was hitherto impossible.
We are still far off from achieving quantum advantage for machine learning,” the IBM researchers, led by Jay Gambetta, write in a blog post. “Yet the feature-mapping methods we’re advancing could soon be able to classify far more complex data sets than anything a classical computer could handle. What we’ve shown is a promising path forward.”