Learn Quantum Machine Learning

A notebook-based course connecting quantum models with modern machine learning.

Overview

Understand the project, then explore the work.

Resources open inside this site. Guides, papers, figures, notebooks, and source files are organized around the project’s story.

About this project

This course will take you through the basic theory required to understand quantum machine learning.

Getting Started (Notes and Coding tutorials)

Here you can discover the basic tools needed to use PennyLane through simple demonstrations. Learn about training a circuit to rotate a qubit, machine learning tools to optimize quantum circuits, and introductory examples of photonic quantum computing.

Optimization

Here you will find demonstrations showcasing quantum optimization. Explore various topics and ideas, such as the shots-frugal Rosalin optimizer, the variational quantum thermalizer, or barren plateaus in quantum neural networks.

Collections

Module

Course lessons

18 curated items

NotebookAdjoint Differentiation NotebookClassical And Quantum Probabilities NotebookData Reuploading Classifier NotebookEvolution In Closed And Open Systems NotebookGalaxy Detection Using QNN NotebookGaussian Transformation

Supporting material in Course lessons12 items

NotebookHybrid Computation NotebookIntro To QAOA NotebookIntroduction To Qiskit NotebookMathematical Introduction NotebookMeasurement And Mixed States NotebookNoisy Cricuits NotebookQuantum Gradients With Backpropagation NotebookQuantum Models As Fourier Series NotebookQubit Rotation NotebookRunning Pennylane In AWS Bracket NotebookVariational Classifier Iris NotebookWhat Is Quantum Machine Learning