Having Trouble Understanding Quantum Machine Learning?

Implementing the Quantum Approximate Optimization Algorithm using functional programming

Do you want to get started with Quantum Machine Learning? Have a look at Hands-On Quantum Machine Learning With Python.

This article will explain the most important parts of the Quantum Approximate Optimization Algorithm (QAOA). QAOA is a machine learning algorithm that you can use to solve combinatorial optimization problems.

The special thing is this algorithm caters to the specificities of quantum computers — a new kind of computer that promises exponential speedups in problem-solving.

Even though quantum machine learning (QML) — that is, using quantum computing to solve machine learning algorithms — is one of the most promising technologies, it is as challenging!

Therefore, this article aims to explain the concepts underlying QAOA in an accessible way.

Quantum computing, optimization, and machine learning rely heavily on mathematics. Unless you’re a mathematician, it will be a daunting endeavor.

Fortunately, some QML libraries, such as IBM Qiskit, solve this problem. They provide easy-to-use interfaces and hide all the complexity from you.

As I showed in my previous post, they even take care of the problem formulation.