Quantum Computing and Quantum Machine Learning - Part 3

Quantum Computing and Quantum Machine Learning - Part 3

Quantum Computing and Quantum Machine Learning - Part 3 ,   is the continuation from what was taught in Part 1 and Part 2. This is going to be the new era of computation/ physics. Enroll for an enriching career in Quantum Research and learn Pythonic Libraries like Qiskit to operate with Quantum Gates and Quantum Circuits in depth. A fantastic computing era to join. In this course will see how to generate quantum circuits using quantum gates like CNOT, Hadamard, SWAP etc. This course sets the correct path in order to study Quantum Cryptography in depth and in the later series will move towards Quantum Machine Learning and libraries of Google like CIRQ.

Will see how to handle quantum circuits using quantum as well as classical channel. Applications of Quantum Teleportation and Super Dense Coding and a very important theorem called as No Cloning Theorem.  Quantum computing is the use of quantum phenomena such as superposition and entanglement to perform computation. Computers that perform quantum computations are known as quantum computers.

In the classical view, one entry would have a value of 1 (i.e. a 100% probability of being in this state) and all other entries would be zero. In quantum mechanics, probability vectors are generalized to density operators. This is the technically rigorous mathematical foundation for quantum logic gates, but the intermediate quantum state vector formalism is usually introduced first because it is conceptually simpler.

Learn great concepts like Quantum Teleportation, Super Dense Coding and IBM's qiskit toolkit

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What you will learn

• Quantum Physics
• Quantum Computing
• Quantum Machine Learning

Rating: 3.75

Level: Intermediate Level

Duration: 2 hours

Instructor: Rushabh Doshi