Quantum machine learning & computing: coding the future

Unlock the secrets of quantum machine learning. A practical guide into QML

Trust me, you don't need to be a quantum physicist to learn quantum machine learning, I wasn't either.

That's why in my teaching, I show the theory through coding examples.

From qubits to superposition and tensor products, we are going to build the fundamentals from the bottom-up. Even if you have tried before but had a hard time understanding the theory,  I guarantee you that this course will give you a thorough understanding of quantum computing. Only the practical stuff are going to be used.

This course is not about the typical quantum algorithms which need thousand of qubits, but rather, quantum machine learning algorithms which can be used today.

It consists of three sections:

1. First, the theory, which is the most important one.

2. Second, the quantum bayesian networks, which span from random number generation to probabilistic modeling.

3. Finally, quantum neural networks, using both qiskit and pytorch, will be the heart of this lecture.

The Quantum Neural Networks are going to be shown with both qiskit and pytorch. The biggest advantage of this lecture is that, the pytorch version of quantum neural networks are going to be constructed using qutrits (rather than qubits), which in my experience, push the boundaries of QML to new heights.

So, are you ready to crack the code of machine learning with quantum computers?