QC101 Quantum Computing & Intro to Quantum Machine Learning
QC101 Quantum Computing & Intro to Quantum Machine Learning, available at $84.99, has an average rating of 4.57, with 304 lectures, based on 2765 reviews, and has 15351 subscribers.
You will learn about Use quantum cryptography to communicate securely Develop, simulate, and debug quantum programs on IBM Qiskit and Microsoft Q# Run quantum programs on a real quantum computer through IBM Quantum Experience Use Dirac's notation and quantum physics models to analyze quantum circuits Train a Quantum Support Vector Machine (Quantum Machine Learning) on real-world data and use it to make predictions Learn Data science and how quantum computing can help in artificial intelligence / machine learning Learn why machine learning will be the killer-app for quantum computing This course is ideal for individuals who are Software professionals and technical managers who want to learn quantum computing and enjoy Math & Physics or Machine Learning and AI professionals who want to learn how quantum computing can be used in data science It is particularly useful for Software professionals and technical managers who want to learn quantum computing and enjoy Math & Physics or Machine Learning and AI professionals who want to learn how quantum computing can be used in data science.
Enroll now: QC101 Quantum Computing & Intro to Quantum Machine Learning
Summary
Title: QC101 Quantum Computing & Intro to Quantum Machine Learning
Price: $84.99
Average Rating: 4.57
Number of Lectures: 304
Number of Published Lectures: 286
Number of Curriculum Items: 304
Number of Published Curriculum Objects: 286
Original Price: $24.99
Quality Status: approved
Status: Live
What You Will Learn
- Use quantum cryptography to communicate securely
- Develop, simulate, and debug quantum programs on IBM Qiskit and Microsoft Q#
- Run quantum programs on a real quantum computer through IBM Quantum Experience
- Use Dirac's notation and quantum physics models to analyze quantum circuits
- Train a Quantum Support Vector Machine (Quantum Machine Learning) on real-world data and use it to make predictions
- Learn Data science and how quantum computing can help in artificial intelligence / machine learning
- Learn why machine learning will be the killer-app for quantum computing
Who Should Attend
- Software professionals and technical managers who want to learn quantum computing and enjoy Math & Physics
- Machine Learning and AI professionals who want to learn how quantum computing can be used in data science
Target Audiences
- Software professionals and technical managers who want to learn quantum computing and enjoy Math & Physics
- Machine Learning and AI professionals who want to learn how quantum computing can be used in data science
Welcome to the bestselling quantum computing course on Udemy!
Quantum Computing is the next wave of the software industry. Quantum computers are exponentially faster than classical computers of today. Problems that were considered too difficult for computers to solve, such as simulation of protein folding in biological systems, and cracking RSA encryption, are now possible through quantum computers.
How fast are Quantum Computers? A 64-bit quantum computer can process 36 billion billion bytes of information in each step of computation. Compare that to the 8 bytes that your home computer can process in each step of computation!
Companies like Google, Intel, IBM, and Microsoft are investing billions in their quest to build quantum computers. If you master quantum computing now, you will be ready to profit from this technology revolution.
This course teaches quantum computing from the ground up. The only background you need is 12th grade level high-school Math and Physics.
IMPORTANT: You mustenjoy Physics and Math to get the most out of this course. This course is primarily about analyzing the behavior of quantum circuits using Math and Quantum Physics. While everything you need to know beyond 12th grade high school science is explained here, you must be aware that Quantum Physics is an extremely difficult subject. You might frequently need to stop the video and replay the lesson to understand it.
QUANTUM MACHINE LEARNING
It appears that the killer-app for quantum computing will be machine learning and artificial intelligence.
Quantum machine learning algorithms provide a significant speed-up in training. This speed-up can result in more accurate predictions.
While understanding quantum algorithms requires mastery of complex math, using quantum machine learning is relatively simple. Qiskit encapsulates machine learning algorithms inside an API that mimics the popular Scikit-Learn machine-learning toolkit. So you can use quantum machine learning almost as easily as you would traditional ML!
Quantum machine learning can be applied in the back-end to train models, and those trained models can be used in consumer gadgets. This means that quantum machine learning might enhance your everyday life even if quantum computers remain expensive!
COURSE OUTLINE
We begin by learning about basic math. You might have forgotten the math you learned in high-school. I will review linear algebra, probability, Boolean algebra, and complex numbers.
Quantum physics is usually considered unapproachable because it deals with the behavior of extremely tiny particles. But in this course, I will explain quantum physics through the behavior of polarized light. Light is an everyday phenomenon and you will be able to understand it easily.
Next we learn about quantum cryptography. Quantum cryptography is provably unbreakable. I will explain the BB84 quantum protocol for secure key sharing.
Then we will learn about the building-blocks of quantum programs which are quantum gates.
To understand how quantum gates work, we will study quantum superposition and quantum entanglement in depth.
We will apply what we have learned by constructing quantum circuits using Microsoft Q# (QSharp) and IBM Qiskit. For those of you who don’t know the Python programming language, I will provide a crisp introduction of what you need to know.
We will begin with simple circuits and then progress to a full implementation of the BB84 quantum cryptography protocol in Qiskit.
We will learn how to use Qiskit’s implementation of Shor’s algorithm for factoring large numbers.
The killer-app for quantum computing is quantum machine learning.
To understand quantum machine learning, we must first learn how classical machine learning works. I provide a crisp introduction to classical machine learning and neural networks (deep learning).
Finally, we will train a Quantum Support Vector Machine on real-world data and use it to make predictions.
For a better learning experience, open the transcript panel.
You will see a small “transcript” button at the bottom-right of the video player on Udemy’s website. If you click this button, the transcript of the narration will be displayed. The transcripts for all the videos have been hand-edited for accuracy. Opening the transcript panel will help you understand the concepts better.
If you missed an important concept, then you can click on text in the transcript panel to return directly to the part you want to repeat. Conversely, if you already understand the concept being presented, you can click on text in the transcript panel to skip ahead in the video.
Enroll today and join the quantum revolution!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: How is Quantum Computing Different?
Chapter 2: Quantum Physics Through Photon Polarization
Lecture 1: Introduction to Quantum Physics
Lecture 2: Quantum Physics Through Photon Polarization 1
Lecture 3: Quantum Physics Through Photon Polarization 2
Lecture 4: Quantum Physics Through Photon Polarization 3
Lecture 5: Quantum Physics Through Photon Polarization 4
Lecture 6: Quantum Physics Through Photon Polarization 5
Lecture 7: Quantum Physics Through Photon Polarization 6
Lecture 8: Quantum Physics Through Photon Polarization 7
Lecture 9: Quantum Physics Through Photon Polarization 8
Lecture 10: Quantum Physics Through Photon Polarization 9
Lecture 11: Quantum Physics Through Photon Polarization 10
Lecture 12: Quantum Physics Through Photon Polarization 11
Lecture 13: Quantum Physics Through Photon Polarization 12
Lecture 14: Quantum Physics Through Photon Polarization 13
Lecture 15: Quantum Physics Through Photon Polarization 14
Chapter 3: Math Foundation: Complex Numbers, Probability, Linear Algebra & Logic
Lecture 1: Quantum Computing Through Math
Lecture 2: Boolean Algebra
Lecture 3: Boolean Variables and Operators
Lecture 4: Truth Tables
Lecture 5: Logic Gates
Lecture 6: Logic Circuits
Lecture 7: AND Gate
Lecture 8: OR Gate
Lecture 9: NOT Gate
Lecture 10: Multiple Input Gates
Lecture 11: Equivalent Circuits 1
Lecture 12: Equivalent Circuits 2
Lecture 13: Universal Gate NAND
Lecture 14: Exclusive OR
Lecture 15: XOR for Assignment
Lecture 16: XOR of Bit Sequences 1
Lecture 17: XOR of Bit Sequences 2
Lecture 18: Introduction to Cryptography
Lecture 19: Cryptography with XOR
Lecture 20: Shared Secret
Lecture 21: Importance of Randomness
Lecture 22: Breaking the Code
Lecture 23: Introduction to Probability
Lecture 24: Probability of a Boolean Expression
Lecture 25: Mutually Exclusive Events
Lecture 26: Independent Events
Lecture 27: Manipulating Probabilities With Algebra
Lecture 28: P (Mutually Exclusive Events)
Lecture 29: P (Independent Events)
Lecture 30: Complete Set of MutEx Events
Lecture 31: P ( A OR B )
Lecture 32: Examples
Lecture 33: Examples
Lecture 34: P ( Bit Values )
Lecture 35: Analysis With Venn Diagrams
Lecture 36: Venn Diagram : P (A AND B)
Lecture 37: Venn Diagram : P (A OR B)
Lecture 38: Venn Diagram : P ( NOT A )
Lecture 39: Examples
Lecture 40: Examples
Lecture 41: Conditional Probability
Lecture 42: Examples
Lecture 43: Introduction to Statistics
Lecture 44: Random Variables
Lecture 45: Mapping Random Variables
Lecture 46: Mean, Average, Expected Value, …
Lecture 47: Example
Lecture 48: Example
Lecture 49: Beyond Mean
Lecture 50: Standard Deviation
Lecture 51: Examples
Lecture 52: Combinations of Random Variables
Lecture 53: Correlation
Lecture 54: Analysis of Correlation
Lecture 55: Introduction to Complex Numbers
Lecture 56: Imaginary i
Lecture 57: Addition
Lecture 58: Subtraction
Lecture 59: Multiplication by a Real
Lecture 60: Division by a Real
Lecture 61: Complex Multiplication
Lecture 62: Examples
Lecture 63: Complex Conjugates
Lecture 64: Squared Magnitude
Lecture 65: Complex Division
Lecture 66: Examples
Lecture 67: Euler's Formula
Lecture 68: Polar Form
Lecture 69: Examples
Lecture 70: Fractional Powers
Lecture 71: Complex Cube Roots of 1
Lecture 72: Square Root of i
Lecture 73: 2D Coordinates
Lecture 74: Matrices
Lecture 75: Matrix Dimensions
Lecture 76: Matrix Addition
Lecture 77: Matrix Subtraction
Lecture 78: Scalar Multiplication
Lecture 79: Matrix Multiplication
Lecture 80: Examples
Instructors
-
Kumaresan Ramanathan
Principal Architect at Coroman Systems
Rating Distribution
- 1 stars: 32 votes
- 2 stars: 62 votes
- 3 stars: 224 votes
- 4 stars: 1022 votes
- 5 stars: 1425 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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