Quantum Computing and Quantum Machine Learning – Part 2

Quantum Computing and Quantum Machine Learning – Part 2, available at $19.99, has an average rating of 4.6, with 28 lectures, based on 69 reviews, and has 571 subscribers.

You will learn about Quantum Mechanics Quantum Physics Quantum Computing Quantum Machine Learning Algebra Calculus Programming Python Quantum Gates Electronics Machine Learning Data Science Artificial Intelligence Quantum Machine Learning Physics Mathematics Qiskit Cirq Quantum Programming Analytics This course is ideal for individuals who are Developers or Physicist or Data Scientist or Machine Learning Engineer or Artificial Intelligence or Engieer or IT or Python Programmers or Data Scientists or Researchers It is particularly useful for Developers or Physicist or Data Scientist or Machine Learning Engineer or Artificial Intelligence or Engieer or IT or Python Programmers or Data Scientists or Researchers.

Enroll now: Quantum Computing and Quantum Machine Learning – Part 2

Summary

Title: Quantum Computing and Quantum Machine Learning – Part 2

Price: $19.99

Average Rating: 4.6

Number of Lectures: 28

Number of Published Lectures: 28

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 28

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Quantum Mechanics
Quantum Physics
Quantum Computing
Quantum Machine Learning
Algebra
Calculus
Programming
Python
Quantum Gates
Electronics
Machine Learning
Data Science
Artificial Intelligence
Quantum Machine Learning
Physics
Mathematics
Qiskit
Cirq
Quantum Programming
Analytics

Who Should Attend

Developers
Physicist
Data Scientist
Machine Learning Engineer
Artificial Intelligence
Engieer
IT
Python Programmers
Data Scientists
Researchers

Target Audiences

Developers
Physicist
Data Scientist
Machine Learning Engineer
Artificial Intelligence
Engieer
IT
Python Programmers
Data Scientists
Researchers

Please ensure you have completed the Part 1 course which sets the foundational tone for this part 2 series

This course sets the correct foundation for learning Quantum Computing and Quantum Machine Learning. Machine Learning, Artificial Intelligence, Physicists, Researchers, Cloud Computing Professionals, Python Programmers, DevOps , Security and Data Science Professionals would cherish this course to join the new era of computing. In this course all the pre-requisites would be covered in depth, so that in the forth coming series of quantum computing and machine learning one can grasp the concepts pretty well

This Quantum Computing Series will have multiple parts and will be launched in segments. It will start from the very basics.

No pre-requisites as such is assumed for this course.

Part 1 will lay down the foundations to study quantum computation.

So part 1 will be mostly quantum mechanics and some mathematical foundations to study this course

From part 2 onward the programming will begin inside using Qiskit library of IBM and gradually more important concepts of quantum computing and quantum machine learning will be unearthed.

Multiple parts of quantum computing series will be launched step wise keeping concepts in certain sections and segregated it will be stepwise progression and gradually building the concepts around quantum computing and quantum machine learning.

This course would build solid foundation for Quantum Computing or anyone who would like to pursue further in this field. This course will introduce you to Quantum Computing/ Programming/ Physics/ Qiskit Framework and Quantum Gates

Pre-requisites:

Python

10th Grade Mathematics/Physics

Please ensure you have completed the Part 1 course which sets the foundational tone for this part 2 series

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Introduction to Part 2 Series

Lecture 1: Introduction to Modern Quantum Mechanics

Chapter 3: Quantum Computing Blog

Lecture 1: Quantumener Blog

Chapter 4: Mathematical Foundations

Lecture 1: Eigen Values and Eigen Vector

Chapter 5: Quantum Principles

Lecture 1: Introduction to Superposition

Lecture 2: Bit Vs Qubit – Application of Superposition Principle

Lecture 3: Application of Superposition Principle

Lecture 4: Geometrical Interpretation of Quantum State

Lecture 5: Probability Measurement of Quantum State on Arbitrary Basis

Lecture 6: Two Qubit System

Lecture 7: Entanglement

Chapter 6: Quantum Gates

Lecture 1: Introduction to Quantum Gates and Quantum NOT Gate

Lecture 2: Hadamard Gate

Lecture 3: Pauli X Gate (NOT Gate)

Lecture 4: Pauli Y Gate (Phase Shift Gate)

Lecture 5: Pauli Z Gate (Phase Flip Gate)

Lecture 6: Controlled NOT Gate (cNOT Gate)

Chapter 7: Bloch Sphere

Lecture 1: Bloch Sphere – Part 1

Lecture 2: Bloch Sphere – Part 2

Chapter 8: Tensor Product

Lecture 1: Tensor Product

Chapter 9: Quantum Programming with IBM's Qiskit

Lecture 1: Qiskit Introduction

Lecture 2: Installation of Anaconda

Lecture 3: Setting up an IBM QX Account

Lecture 4: Practical – 1

Lecture 5: Practical – 2

Lecture 6: Practical – 3

Chapter 10: Conclusion

Lecture 1: Conclusion

Chapter 11: Part 3 is Out

Lecture 1: Part 3 is Launched

Instructors

Rushabh Doshi
Researcher

Rating Distribution

1 stars: 2 votes
2 stars: 4 votes
3 stars: 9 votes
4 stars: 20 votes
5 stars: 34 votes

Frequently Asked Questions

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