Quantum Computing and Quantum Machine Learning - Part 3

Category: Tutorial

Posted on 2021-01-30, by 0nelovee.


Quantum Computing and Quantum Machine Learning - Part 3
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 24 lectures (1h 52m) | Size: 394.2 MB

Learn great concepts like Quantum Teleportation, Super Dense Coding and IBM's qiskit toolkit What you'll learn:
Quantum Physics
Quantum Computing
Quantum Machine Learning
Quantum Gates
Machine Learning
Data Science
Artificial Intelligence

Basic Python
Quantum Computing and Quantum Machine Learning - Part 1
Quantum Computing and Quantum Machine Learning - Part 2

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.

Who this course is for
Data Scientists
Machine Learning Engineer
Artificial Intelligence Researchers
Data Engineer
Deep Learning
Deep Learning Engineers
Reinforcement Learning
Python Developers



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Quantum Computing and Quantum Machine Learning - Part 3

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