# 2021 Qiskit Global Summer School

**Link URL**::
https://qiskit.org/textbook-beta/summer-school/quantum-computing-and-quantum-learning-2021/**Links**:: Quantum Machine Learning

About the course

The Qiskit Global Summer School 2021 was a two-week intensive summer school designed to empower the next generation of quantum researchers and developers with the skills and know-how to explore quantum applications on their own. This second-annual course, made up of twenty lectures, five applied lab exercises, hands-on mentorship, and live Q&A sessions, focused on developing hands-on experience and understanding of quantum machine learning.

## # Lectures

- Lecture 1.1 - Vector Spaces, Tensor Products, and Qubits
- Lecture 1.2 - Introduction to Quantum Circuits
- Lecture 2.1 - Simple Quantum Algorithms I
- Simple Quantum Algorithms II
- Noise in Quantum Computers pt 1
- Noise in Quantum Computers pt. 2
- Lab 1: Quantum Computing Algorithms and Operations
- Introduction to Classical Machine Learning
- Advanced Classical Machine Learning
- Lecture 5.1 - Building a Quantum Classifier
- Introduction to the Quantum Approximate Optimization Algorithm and Applications
- Lab 2: Variational Algorithms
- From Variational Classifiers to Linear Classifiers
- Quantum Feature Spaces and Kernels
- Quantum Kernels in Practice
- Lab 3: Introduction to Quantum Kernels and Support Vector Machines
- Introduction and Applications of Quantum Models
- Barren Plateaus, Trainability Issues, and How to Avoid Them
- Lab 4: Introduction to Training Quantum Circuits
- Introduction to Quantum Hardware
- Hardware Efficient Ansatze for Quantum Machine Learning
- Lab 5: Introduction to Hardware Efficient Ansatze for Quantum Machine Learning
- Advanced QML Algorithms: Quantum Boltzmann Machines and Quantum Generative Adversarial Networks
- The Capacity and Power of Quantum Machine Learning Models & the Future of Quantum Machine Learning