列出的学习单元属于课程Quantum Machine Learning (with IBM Quantum)。您想访问所有课程内容吗？
Intro
This course aims at enabling you to discover the field of Quantum Machine Learning. You will learn about the basics, such as parameterized quantum models and training algorithms, investigate promising models which are compatible with today's quantum hardware, and learn how to write Quantum Machine Learning algorithms by yourself with Qiskit.
Week 1
After giving an overview answering the question: "What is Quantum Machine Learning?", we will present a general introduction to machine learning followed by a deep-dive into Support Vector Machines and their quantum counter-part Quantum Support Vector Machines. Finally, we present a variational Quantum Machine Learning classification algorithm called the Variational Quantum Classifier.
Week 2
In the second week of the course, we will firstly discuss how Quantum Machine Learning models are being trained. Then, we have a closer look at two specific models, i.e., Quantum Generative Adversarial Networks and Quantum Boltzmann machines. Furthermore, we give a practical coding introduction to Machine Learning with Qiskit.
Lastly, we explore the potential of Quantum Machine Learning in a discussion with an expert in the field.
Training Quantum Machine Learning Models
视频
Module 6: Training
自我测试
Approximate Data Loading with Quantum Generative Adversarial Networks
视频
Module 7: Quantum Generative Adversarial Networks
自我测试
Quantum Boltzmann Machines
视频
Module 8: Quantum Boltzmann Machines
自我测试
Machine Learning with Qiskit
视频
Module 9: Machine Learning with Qiskit
自我测试
Dr. David Sutter
视频
Module 10: Potential
自我测试
Slides week 2
文本
Final exam
We hope you enjoyed this course on Quantum Machine Learning and wish you good luck for the final exam!