QC101 Quantum Computing & Intro to Quantum Machine Learning from Udemy

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 must enjoy 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 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.

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.

What's inside

Learning objectives

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

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

Syllabus

Introduction

How is Quantum Computing Different?

Learn about superposition and entanglement through photon polarization

Introduction to Quantum Physics

Quantum Physics Through Photon Polarization 1

Quantum Physics Through Photon Polarization 2

Quantum Physics Through Photon Polarization 3

Quantum Physics Through Photon Polarization 4

Quantum Physics Through Photon Polarization 5

Quantum Physics Through Photon Polarization 6

Quantum Physics Through Photon Polarization 7

Quantum Physics Through Photon Polarization 8

Quantum Physics Through Photon Polarization 9

Quantum Physics Through Photon Polarization 10

Quantum Physics Through Photon Polarization 11

Quantum Physics Through Photon Polarization 12

Quantum Physics Through Photon Polarization 13

Quantum Physics Through Photon Polarization 14

Know enough Math to begin learning Quantum Physics and Quantum Computing

Quantum Computing Through Math

Boolean Algebra

Boolean Variables and Operators

Truth Tables

Logic Gates

Logic Circuits

AND Gate

OR Gate

NOT Gate

Multiple Input Gates

Equivalent Circuits 1

Equivalent Circuits 2

Universal Gate NAND

Exclusive OR

XOR for Assignment

XOR of Bit Sequences 1

XOR of Bit Sequences 2

Introduction to Cryptography

Cryptography with XOR

Shared Secret

Importance of Randomness

Breaking the Code

Introduction to Probability

Probability of a Boolean Expression

Mutually Exclusive Events

Independent Events

Manipulating Probabilities With Algebra

P (Mutually Exclusive Events)

P (Independent Events)

Complete Set of MutEx Events

P ( A OR B )

Examples

P ( Bit Values )

Analysis With Venn Diagrams

Venn Diagram : P (A AND B)

Venn Diagram : P (A OR B)

Venn Diagram : P ( NOT A )

Conditional Probability

Introduction to Statistics

Random Variables

Mapping Random Variables

Mean, Average, Expected Value, ...

Example

Beyond Mean

Standard Deviation

Combinations of Random Variables

Correlation

Analysis of Correlation

Introduction to Complex Numbers

Imaginary i

Addition

Subtraction

Multiplication by a Real

Division by a Real

Complex Multiplication

Complex Conjugates

Squared Magnitude

Complex Division

Euler's Formula

Polar Form

Fractional Powers

Complex Cube Roots of 1

Square Root of i

2D Coordinates

Matrices

Matrix Dimensions

Matrix Addition

Matrix Subtraction

Scalar Multiplication

Matrix Multiplication

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces learners to the basics of quantum computing, making it suitable for beginners new to the field

Provides an in-depth overview of how quantum physics and quantum computing work using simple and clear examples

Offers a comprehensive understanding of various quantum concepts like superposition, entanglement, and quantum gates, providing a solid foundation for learners

Utilizes multiple programming languages, including Python and Q#, allowing learners to choose the most familiar

Provides hands-on experience through real-world applications like quantum cryptography and machine learning, making the learning process more engaging

Focus on the killer-app of quantum computing - quantum machine learning through real-world data analysis

Reviews summary

Quantum computing in practice

Learners say they find this course very good and interestingly presented. Students appreciate that the course is taught in a clear and intelligent way, especially given that English may not be the instructor's first language.

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in QC101 Quantum Computing & Intro to Quantum Machine Learning with these activities:

Review linear algebra

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Linear algebra underpins most operations in quantum computing. A refresher will make this course easier to grasp.

Browse courses on Linear Algebra

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Review matrices, vectors, and dot products.
Practice solving linear equations.
Review eigenvalues and eigenvectors.

Read Quantum Computing for Beginners

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This book is a gentle introduction to quantum computing that will help you understand the basics.

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Read the introduction and first chapter.
Review the key concepts.

Complete quantum circuit exercises

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Practice is key to mastering quantum computing. These exercises will help you apply the concepts you learn in the course.

Browse courses on Quantum Circuits

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Solve exercises on basic quantum gates.
Design and simulate quantum circuits.

Three other activities

Expand to see all activities and additional details

Show all six activities

Join a study group

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Discussing the course material with peers can help you to better understand the concepts.

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Find a study group or start your own.
Meet regularly to discuss the course material.

Build a quantum computing project

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Building a project is a great way to apply your skills and learn new ones.

Browse courses on Quantum Algorithms

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Choose a project to work on.
Design and implement the project.
Test and debug the project.
Write a report on the project.

Contribute to an open-source quantum computing project

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Contributing to an open-source project is a great way to learn about quantum computing and contribute to the community.

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Find an open-source quantum computing project to contribute to.
Read the project's documentation.
Make a contribution to the project.

Career center

Learners who complete QC101 Quantum Computing & Intro to Quantum Machine Learning will develop knowledge and skills that may be useful to these careers:

Quantum Computing Researcher

Quantum Computing Researchers focus on developing new theoretical and practical aspects of quantum computing. They often publish their findings in academic journals and conferences. Quantum Computing & Intro to Quantum Machine Learning can help Quantum Computing Researchers to build a solid foundation in the field and to stay up-to-date on the latest developments.

See salaries and explore the career path for Quantum Computing Researcher

Quantum Software Engineer

Quantum Software Engineers design, develop, and maintain software for quantum computers. They work on developing new quantum programming languages and tools, as well as optimizing existing software for quantum hardware. Quantum Computing & Intro to Quantum Machine Learning can help Quantum Software Engineers to build a strong foundation in the field and to stay up-to-date on the latest developments.

See salaries and explore the career path for Quantum Software Engineer

Quantum Information Scientist

Quantum Information Scientists study the fundamental principles of quantum mechanics and how they can be applied to information processing. They work on developing new quantum algorithms and protocols for tasks such as cryptography, communication, and computing. Quantum Computing & Intro to Quantum Machine Learning can help Quantum Information Scientists to develop the skills and knowledge that they need to succeed in this field.

See salaries and explore the career path for Quantum Information Scientist

Quantum Cryptographer

Quantum Cryptographers develop and implement quantum cryptography protocols to secure communications. They work on developing new quantum key distribution schemes and protocols, as well as designing and implementing quantum-safe cryptographic algorithms. Quantum Computing & Intro to Quantum Machine Learning can help Quantum Cryptographers to build a strong foundation in the field and to stay up-to-date on the latest developments.

See salaries and explore the career path for Quantum Cryptographer

Data Scientist

Data Scientists use advanced computing knowledge to extract insights from data. These professionals use complex algorithms to sort through large datasets with the goal of identifying trends and correlations. Quantum Computing & Intro to Quantum Machine Learning can help Data Scientists to develop the skills that they need to analyze large and complex datasets in the field, expediting their work and potentially leading to new discoveries.

See salaries and explore the career path for Data Scientist

Quantum Physicist

Quantum Physicists study the fundamental principles of quantum mechanics and how they apply to the real world. They work on developing new theories and models for quantum phenomena, as well as developing new experimental techniques for studying quantum systems. Quantum Computing & Intro to Quantum Machine Learning can help Quantum Physicists to build a solid foundation in the field and to stay up-to-date on the latest developments.

See salaries and explore the career path for Quantum Physicist

Physicist

Physicists study the fundamental laws of nature. They work on developing new theories and models for physical phenomena, as well as developing new experimental techniques for studying physical systems. Quantum Computing & Intro to Quantum Machine Learning may be useful to Physicists who are interested in exploring the potential of quantum computing for physics.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They work on developing new software products, as well as maintaining and updating existing software. Quantum Computing & Intro to Quantum Machine Learning may be useful to Software Engineers who are interested in exploring the potential of quantum computing for software development.

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Mathematician

Mathematicians study the properties of numbers, shapes, and other abstract objects. They work on developing new mathematical theories and models, as well as solving mathematical problems. Quantum Computing & Intro to Quantum Machine Learning may be useful to Mathematicians who are interested in exploring the potential of quantum computing for mathematics.

See salaries and explore the career path for Mathematician

Computer Scientist

Computer Scientists conduct research on the theory and practice of computing. They work on developing new computer architectures, algorithms, and programming languages, as well as studying the social and ethical implications of computing. Quantum Computing & Intro to Quantum Machine Learning may be useful to Computer Scientists who are interested in exploring the potential of quantum computing for computer science.

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Scientist

Scientists conduct research on the natural world. They work on developing new theories and models for natural phenomena, as well as developing new experimental techniques for studying natural systems. Quantum Computing & Intro to Quantum Machine Learning may be useful to Scientists who are interested in exploring the potential of quantum computing for science.

See salaries and explore the career path for Scientist

Professor

Professors teach and conduct research at colleges and universities. They work on developing new knowledge and theories, as well as educating students. Quantum Computing & Intro to Quantum Machine Learning may be useful to Professors who are interested in exploring the potential of quantum computing for teaching and research.

See salaries and explore the career path for Professor

Engineer

Engineers design, build, and maintain machines, structures, and systems. They work on developing new technologies, as well as improving existing technologies. Quantum Computing & Intro to Quantum Machine Learning may be useful to Engineers who are interested in exploring the potential of quantum computing for engineering.

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Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. They use this information to help businesses make better decisions. Quantum Computing & Intro to Quantum Machine Learning may be useful to Data Analysts who are interested in exploring the potential of quantum computing for data analysis.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models. They work on developing new machine learning algorithms and techniques, as well as optimizing existing algorithms for specific applications. Quantum Computing & Intro to Quantum Machine Learning may be useful to Machine Learning Engineers who are interested in exploring the potential of quantum computing for machine learning.

See salaries and explore the career path for Machine Learning Engineer