MarcinPlodzien / Quantum-Machine-Learning-Introduction

Lectures on hybrid quantum-classical machine learning given during "VI Pyrenees Winter School Quantum Information Meeting for Barcelona's Community" on 14-17.02.2023, Setcases, Spain

Introduction to Hybrid Quantum-Classical Machine Learning methods in Quantum Computing

This repository contains lectures I gave during the "VI Pyrenees Winter School Quantum Information Meeting for Barcelona's Community" (Setcases, Spain, 14-17.02.2023).

Outline:

1. Introduction to Automatic Differentiation
2. Implementing quantum gates from scratch: Simulating circuits and qubit gates
3. Example: Variational Quantum Eigensolver (VQE) implementation from scratch with PyTorch
4. Example: Quantum Approximate Optimization Algorithm (QAOA) implementation from scratch with PyTorch

For a comprehensive introduction to machine learning for quantum technologies, see: "Modern applications of machine learning in quantum sciences" https://arxiv.org/abs/2204.04198