Explore your interest in learning quantum machine learning


Quantum machine learning is a future-changing merger of quantum computing and artificial intelligence. It’s a research topic devoted to the development of quantum algorithms for machine learning problems. Quantum machine learning is projected to be a viable use of quantum computers in the future years.

How to Begin Quantum Machine Learning- Coursera

This 2-hour project-based course will show you the principles of how machine learning may benefit from work and how to use the Xanadu Pennylane library in Python to do so.

In this project, you may learn how to utilize a variety of software libraries to design quantum algorithms and encode data for use in both classical-quantum device simulations and genuine quantum devices available for use via the Internet from vendors like IBM.

Machine Learning with Quantum Dimensions- edX

This course aims to show how present and future quantum technologies might improve machine learning, with a focus on algorithms that are challenging to execute on ordinary digital computers. The usage of open-source Python frameworks to implement the protocols is emphasized.

Quantum Machine Learning and Quantum Computing- Part 1- Udemy

The foundations of quantum computing and quantum machine learning are laid forth in this course. This course is designed for professionals interested in Machine Learning, Artificial Intelligence, Physicists, Researchers, Cloud Computing Professionals, Python Programmers, DevOps, Security, and Data Science.

Machine Learning with Quantum Dimensions- Blue courses

This course will teach participants the principles of quantum computing. Quantum computing insight frequently demands mathematics insight. The course requires a medium to the strong theoretical background, but it is also appropriate for students who have little or no prior knowledge of quantum mechanics or quantum computing.

For Data Scientists: Quantum Machine Learning- arXiv

Fundamental quantum theory is covered in the first portion, while quantum computation and quantum computer design are covered in the second. In the third part, quantum algorithms are introduced as subroutines in quantum machine learning methods. Finally, the fourth component discusses quantum machine learning methods employing the knowledge obtained in the previous parts.

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