Quantum Computing Should Supercharge This Machine Learning Technique

Quantum Computing Should Supercharge This Machine Learning Technique

The MIT Technology Review has an interesting article on one specific way that quantum computing can revolutionize machine learning.

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.

Quantum Computers Can Do Machine Learning – We Just Need To Build One That Works

Quantum Computers Can Do Machine Learning – We Just Need To Build One That Works

Researchers at IBM have drafted some new algorithms designed specifically to take advantage of quantum computers’ unique properties. The only catch is that we still need to build the computer.

While designing algorithms before the computers themselves may sound backwards, this has happened before. Computational models for conventional computers date back to the 1800s when Charles Babbage and Ada Lovelace were pondering mechanical computing devices. 

From the article:

“We’ve developed a blueprint with new quantum data classification algorithms and feature maps. That’s important for AI because, the larger and more diverse a data set is, the more difficult it is to separate that data out into meaningful classes for training a machine learning algorithm. Bad classification results from the machine learning process could introduce undesirable results; for example, impairing a medical device’s ability to identify cancer cells based on mammography data.”

IBM has come up with a way to use quantum computers to improve machine learning algorithms, even though we don’t have anything approaching a quantum computer yet. The tech giant developed and tested a quantum algorithm for machine learning with scientists from Oxford University and MIT, showing how quantum […]