Making Sense Of Quantum Machine Learning

The rocket science of the 21st century?

Quantum Machine Learning (QML) is often hailed as the "rocket science" of the 21st century. A fusion of the exotic capabilities of quantum computing with the problem-solving power of machine learning.

As advances in classical AI have astounded us with ever more impressive feats, it's tempting to imagine quantum-accelerated intelligence catapulting us into a future of unlimited possibilities. Or dystopian peril.

But despite the hype, QML remains shrouded in mystery and is poorly understood.

And that's no surprise. Just take a look at the equation below, which maps the entire QML pipeline of encoding, variational circuit, measurement, cost evaluation, and parameter update.

Unless you are already an experienced quantum scientist, such an equation is of no use to you. It's as good as Egyptian hieroglyphics

It is the language of quantum computing and machine learning. It is the home of the ghoul of the unexplained equation.

But there is another way to learn QML. Instead of diving straight into complex mathematical formalisms, let's first get an overview of the conceptual QML "rocket". Just as a rocket is made up of different but interconnected systems to soar, QML algorithms are made up of parts that work together to turn data into insights.