Quantum machine learning aims to apply quantum algorithms to improve machine learning methods to solve real life problems 

Topics

1. Classical Machine Learning

   • Lecture: Introduction to Classical Machine Learning(Qiskit Global Summer School 2021) Lecture 4.1~4.2

   • Textbook: Chapter 1.3

     • Nearest Neighbor

     • Support Vector Machines

     • Deep Learning

   • Supplementary Materials(Optional):

     • Andrew Ng CS229 Lecture Notes: Chapter 5~6

2. Variational Quantum Algorithms

   • Lectures:  Qiskit Global Summer School 2021

     • Building a Quantum Classifier: Lecture 5.1

     • Introduction to the Quantum Approximate Optimization Algorithm and Applications: Lecture 5.2

   • Labs:

     •  Qiskit Global Summer School Lab 2: QAOA(https://github.com/Qiskit/platypus/blob/main/notebooks/summer-school/2021/resources/lab-notebooks/lab-2.ipynb)

     • Qiskit Global Summer School Lab 4: Training Parametrized Circuits(https://github.com/Qiskit/platypus/blob/main/notebooks/summer-school/2021/resources/lab-notebooks/lab-4.ipynb)

   • Tutorials:

     • Qiskit Textbook: Training Parametrized Circuits(https://github.com/Qiskit/textbook/blob/main/notebooks/quantum-machine-learning/training.ipynb)

     • Qiskit Textbook: Variational Quantum Circuits(https://github.com/Qiskit/textbook/blob/main/notebooks/quantum-machine-learning/vqc.ipynb)

     • Qiskit Tutorials: QAOA(https://github.com/Qiskit/qiskit-tutorials/blob/master/tutorials/algorithms/05_qaoa.ipynb)

3. Quantum Feature Maps / Quantum Encoding

   • Lectures:  Qiskit Global Summer School 2021

     • From Variational Classifiers to Linear Classifiers: Lecture 6.1

     •  Quantum Feature Spaces and Kernels: Lecture 6.2

     • Quantum Kernels in Practice: Lecture 7.1

   • Textbook: 

     • Amplitude Encoding Chapter 16

     • Quantum Kernels Chapter 17

   • Labs:

     • Quantum Explorers 2023 Space Combatant Lab 1(https://github.com/qiskit-community/quantum-explorers/blob/main/Space_Combatant_Badge/2023/QE_Badge5_Lab1.ipynb)

     •  Qiskit Global Summer School Lab 3: Quantum Kernels and Support Vector Machines (https://github.com/Qiskit/platypus/blob/main/notebooks/summer-school/2021/resources/lab-notebooks/lab-3.ipynb)

   • Tutorials: Qiskit Textbook

     • Data Encoding(https://github.com/Qiskit/textbook/blob/main/notebooks/quantum-machine-learning/encoding.ipynb)

     • Kernels (https://github.com/Qiskit/textbook/blob/main/notebooks/quantum-machine-learning/kernel.ipynb)

4. Quantum GANs/Barren Plateaus & Advanced Topics

   • Lectures: Qiskit Global Summer School 2021

     • Introduction and Applications of Quantum Models: Lecture 8.1

     • Barren Plateaus, Trainability Issues, and How to Avoid Them: Lecture 8.2

     • Advanced QML Algorithms: Lecture 10.1

   • Tutorials: Qiskit Textbook

     • Quantum GANs(https://github.com/Qiskit/textbook/blob/main/notebooks/quantum-machine-learning/qgan.ipynb)