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Top resources to learn quantum machine learning

Quantum computing and machine learning are two of the most exciting technologies that can transform businesses. We can only imagine how powerful it can be if we can combine the power of both of these technologies. When we can integrate quantum algorithms in programs based on machine learning, that is called quantum machine learning. This fascinating area has been a major area of tech firms, and they have brought out tools and platforms to deploy such algorithms effectively. Some of these include TensorFlow Quantum from Google, Quantum Machine Learning (QML) library from Microsoft, QC Ware Forge built on Amazon Braket, etc.

Students skilled in working with quantum machine learning algorithms can be in great demand due to the opportunities the field holds. Let us have a look at a few online courses one can use to learn quantum machine learning.

In this course, the students will start with quantum computing and quantum machine learning basics. The course will also cover topics on building Qnodes and Customised Templates. It also teaches students to calculate Autograd and Loss Function with quantum computing using Pennylane and to develop with the API. The students will also learn how to build their own Pennylane Plugin and turn Quantum Nodes into Tensorflow Keras Layers.

The students will learn how to utilise software libraries to code quantum algorithms and encode data for use in classical simulations of quantum devices or actual quantum devices available for use over the Internet through vendors such as IBM. The course duration is two hours.

Part 1 of the course will set the foundation for learning quantum computing and quantum machine learning. The course is suitable for data science professionals, Python programmers, DevOps professionals, physicists, researchers, mathematicians, big data developers, and IT professionals. Students will learn quantum mechanics, quantum physics, quantum computing, calculus, algebra, Python, etc.

Part 2 of the course will start using the Qiskit library of IBM. Then, it will move into important concepts of quantum computing and quantum machine learning. It will lay down mathematical foundations, quantum principles, quantum gates, Block Sphere, and Tensor Sphere.

The course will move to areas such as libraries of Google, like CIRQ. Students will also learn how to handle quantum circuits using quantum and classical channels. They will learn the applications of Quantum Teleportation and Super Dense Coding and No Cloning Theorem.

This course will teach students the foundational blocks of quantum computing. It will start with the conceptual foundations of quantum systems, move on to basic elementary computational operations, with example programs in Python Qiskit. After learning these fundamental concepts, the course will introduce core quantum computing algorithms, with an emphasis on coherent quantum machine learning. Some topics include Quantum Fourier Transformation, Quantum Phase Estimation and Grover search.

The course will also teach Near-Term Intermediate Scale Quantum devices (NISQ) and derive hybrid quantum-classical algorithms that are suitable for running on current hardware. It will talk about recent breakthroughs and quantum supremacy and discuss existing hardware. To pursue the course, students are expected to have a medium to strong theoretical background, but it is also for people who are unfamiliar with quantum mechanics and quantum computing.


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