Quantum Computing
This course will enable you to
1. Describe quantum machine learning models and differentiate them from classical ML,
2. Implement quantum machine learning algorithms using Qiskit on IBM quantum computers,
3. Encode the classical real-world datasets in quantum states to be used in quantum machine learning algorithms,
4. Implement quantum variational classifiers designed with custom feature maps and trainable parametric circuits,
5. Implement quantum support vector machines for classification tasks with various quantum kernels.
Data Encoding in Quantum States, Quantum Variational Circuits, Quantum Classifiers, Quantum Feature Maps, Quantum Support Vector Machines
Note: This course requires an introductory quantum computing course and prior hands-on experience with Qiskit.
Quantum machine learning is a rapidly emerging frontier of quantum computing that deals with the application of quantum computers in learning from the classical data. As quantum computers transition from theory to reality, this course offers professionals and researcher an opportunity to get ahead of the curve and gain practical expertise in it. This course provides a hands-on introduction and programming experience on actual IBM quantum computers to solve machine learning problems with real world datasets. You will work with IBM’s cloud-based quantum computers and write quantum programs using Qiskit, a Python-based library. Along the way, you will explore how quantum algorithms can be used for classification tasks of real-world high-dimensional datasets. The main topics include quantum machine learning models, data encoding techniques, quantum feature maps, variational quantum classifiers, and quantum support vector machines.
