Quantum Computing and Quantum Machine Learning - Part 4
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 13 lectures (1h 10m) | Size: 231.1 MB

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Learn state of the Quantum Algorithms, Quantum Circuits and Practicals on Qiskit
What you'll learn:
Quantum Computing
Quantum Machine Learning
Algebra
Calculus
Programming
Python
Quantum Gates
Electronics
Machine Learning
Data Science
Artificial Intelligence
Physics

Requirements
Go through the previous 3 parts of Quantum Computing and Quantum Machine Learning Series

Description
Learn state of the Quantum Algorithms, Quantum Circuits and Practicals on Qiskit in the course - Quantum Computing and Quantum Machine Learning - Part 4. Learn some phenomenal concepts such as Fourier Series and Fourier Transform. Ensure you have gone through the pre-requisites, that is you have gone through all the previous parts of Quantum Computing and Quantum Machine Learning i.e. Part 1,2 and 3.

This course will further enhance your understanding about quantum computing and the algorithms that it can solve , there are algorithms such as Deutsch Algorithm, Deutsch Josza Algorithm, Grover's Algorithm, which will see how it can be applied to solve certain challenges which the classical algorithms takes lot of time.

For practicals the library that will see is qiskit and how in real-time use cases these quantum algorithms could be handy.

We will even learn the concept regarding Fourier Series and Fourier Transform, which are immensely used to decode the signal and even check how one signal can be decoded into multiple waves.

In the next series will see algorithms like Shor's Factorization Algorithm which can break the traditional RSA encryption techniques and how one needs to be prepared for Post-Quantum Era. So that in cryptography new algorithms could come and take over. Quantum Cryptography would be an excellent and state of the cryptography which would be unbreakable and cannot be broken. Would be completely a secure and tamper-proof system.

Who this course is for
Physicist
Mathematician
IT Professional
Big Data Developers
Machine Learning Developers
Artificial Intelligence Researchers
Data Scientists
Machine Learning Engineer
Developers
Data Engineer
Researchers
Scientists
Data Scientist

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