When you listen to quantum enthusiasts, you may feel tempted to treat QML as an emerging silver bullet, which it isn’t.

Some say that machine learning is a form of pattern recognition, understanding when a particular pattern occurs in nature or experience or through senses, and then acting on that pattern recognition.

“Machine learning” includes a host of historical techniques which don’t seem so relevant any more, in the age of neural networks, and yet “neural networks” is both too narrow and too broad.

Machine learning is perfect for data analysis, pattern recognition, and prediction, all of which have significance for optimizing operations in industries such as banking, healthcare, and retail.

It’s a great concept, but requires pattern matching, machine learning, and insight. Insight is what lets people take the mental leap into a new area.

Two years ago, physicists at the University of Chicago were greeted with fireworks —atoms shooting out in jets—when they discovered a new form of quantum behavior. But the patterns underlying the ...

Ultimate precision for sensor technology using qubits and machine learning Date: July 3, 2018 Source: Aalto University Summary: Extracting information quickly from quantum states is necessary for ...