Quantum Computing and Quantum Machine Learning

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

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

What's inside

Learning objectives

Quantum mechanics
Quantum physics
Quantum computing
Quantum machine learning
Algebra

Calculus
Programming
Python
Electronics

Quantum mechanics
Quantum physics
Quantum computing
Quantum machine learning
Algebra
Calculus
Programming
Python
Electronics

Syllabus

Introduction

This course is the foundational course on Quantum Computing and Quantum Machine Learning. In this course we would study the difference between classical physics Vs Quantum Physics. Quantum Mechanics concepts which we would need to grasp in order to study Quantum Computing and Quantum Machine Learning. The pre-requisites for this course is 10th Grade Maths thats it, rest all gets covered in this course

This course sets the correct foundation to learn Quantum Computing and Quantum Machine Learning. It's a great course for Developers, Researchers, Physicists, Data Scientists and Machine Learning Engineers

Pre-Requisites Taken Care

At the end of this section students get the basics of what they are going to learn from this course, why quantum mechanics? Important people around Quantum Mechanics

Introduction to the course

Introduction to Modern Quantum Mechanics

Why Quantum Mechanics?

Learn Technology, Life and Spirituality Related Articles

Quantumener Blog

Students would study two basic postulates of Quantum Mechanics in order to set the path for further lectures

Introduction to Postulates of Quantum Mechanics

Postulate 1 - Quantum Mechanics

Postulate 2 - Quantum Mechanics

In this section students would go back to school mathematics and get the fundamentals right in order to study quantum mechanics in depth, which would aid in learning Quantum Computing

Complex Numbers

Matrices - 1

Matrices - 2

Matrices - 3

Matrices - 4

Probability

Calculus - 1

Calculus - 2

Calculus - 3

Understanding of Vectors are the most important thing in Quantum Mechanics and Quantum Computing, as the quantum state is represented as a vector

Vector Algebra 1

Vector Algebra 2

The heart of this course which would lay down correct foundations for learning quantum computing in depth, students would be able to make sense of dirac notations and other quantum concepts

Classical Mechanics Vs Quantum Mechanics

Heisenberg Principle - 1

Heisenberg Principle - 2

Schrodinger Equation 1

Schrodinger Equation 2

Magic Equation - Classical Physics

Magic Equation - Quantum Physics

Normalization of Wave Function - 1

Normalization of Wave Function - 2

Expectation Value

Dirac Notations - 1

Dirac Notations - 2

Dirac Notations - 3

Dirac Notations - 4

Dirac Notations - 5

We conclude with this foundational course, in order to embark on a new journey of quantum computing and quantum machine learning. New series will be announced soon! (Part 2 coming soon)

Conclusion

Bonus Section

Link - https://www.udemy.com/course/quantum-computing-and-quantum-machine-learning-part-2/

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers pre-requisites in depth, which allows learners to grasp complex concepts in quantum computing and machine learning, even without prior experience

Introduces quantum computing, programming, physics, the Qiskit framework, and quantum gates, which builds a solid foundation for further study in the field

Explores the differences between classical and quantum physics, which is essential for understanding the principles behind quantum computing

Requires completion of Part 1, which means that learners must dedicate time to a series of courses to gain a comprehensive understanding

Reviews school mathematics to get the fundamentals right, which aids in learning quantum mechanics in depth and quantum computing

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 Quantum Computing and Quantum Machine Learning - Part 1 with these activities:

Review Linear Algebra Fundamentals

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Reinforce your understanding of linear algebra concepts, including vectors, matrices, and matrix operations, as these are crucial for understanding quantum states and quantum gates.

Browse courses on Linear Algebra

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Review vector addition and scalar multiplication.
Practice matrix multiplication and inversion.
Solve systems of linear equations.

Brush Up on Complex Numbers

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Strengthen your knowledge of complex numbers, as they are fundamental to representing quantum amplitudes and wave functions.

Browse courses on Complex Numbers

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Review the definition of complex numbers.
Practice complex number arithmetic.
Convert between rectangular and polar forms.

Read 'Quantum Computation and Quantum Information'

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Supplement your learning with a deep dive into the core concepts of quantum computation and information theory.

View Quantum Computation and Quantum Information on Amazon

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Read the chapters on quantum mechanics and quantum circuits.
Work through the examples and exercises.
Focus on the sections relevant to the course syllabus.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Solve Quantum Mechanics Problems

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Solidify your understanding of quantum mechanics by working through practice problems related to the topics covered in the course, such as wave functions, operators, and expectation values.

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Find a collection of quantum mechanics problems.
Solve problems related to wave functions and operators.
Check your solutions against the provided answers.

Create a Quantum Computing Glossary

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Reinforce your understanding of key quantum computing terms by creating a glossary of definitions and explanations.

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Identify key terms from the course materials.
Write clear and concise definitions for each term.
Include examples and illustrations where appropriate.

Simulate a Simple Quantum System

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Apply your knowledge by simulating a simple quantum system, such as a qubit or a quantum gate, using Python and a library like NumPy.

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Choose a simple quantum system to simulate.
Write Python code to represent the system's state and evolution.
Test your simulation with different inputs and parameters.

Explore 'Principles of Quantum Mechanics'

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Deepen your understanding of the underlying quantum mechanics principles with a classic textbook.

View Principles of Quantum Mechanics on Amazon

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Focus on chapters covering postulates and operators.
Work through examples related to quantum states.
Relate concepts to quantum computing applications.

Career center

Learners who complete Quantum Computing and Quantum Machine Learning - Part 1 will develop knowledge and skills that may be useful to these careers:

Quantum Computer Scientist

A Quantum Computer Scientist explores the theoretical potential of quantum computers and seeks to develop practical quantum algorithms. This course helps build a strong mathematical and physics foundation, particularly in quantum mechanics, which is vital for this role. Mastery of complex numbers, matrices, calculus, and vector algebra is central to understanding quantum states. The course's introduction to Dirac notation and the Schrodinger equation provides a basic understanding of the scientist's toolkit. Someone interested in becoming a Quantum Computer Scientist should take this course to ensure a solid base of knowledge.

See salaries and explore the career path for Quantum Computer Scientist

Research Scientist

A Research Scientist conducts experiments and develops new theories in a specific scientific field. This course lays a basic foundation in quantum mechanics, a cornerstone of modern physics. The course helps build understanding of complex numbers, matrices, calculus, vector algebra, Dirac notation, and the Schrodinger equation, all of which are essential for research in quantum-related fields. Aspiring Research Scientists should take this course to gain a foothold in a field that is at the cutting edge of scientific discovery.

See salaries and explore the career path for Research Scientist

Quantum Machine Learning Researcher

A Quantum Machine Learning Researcher investigates how quantum computing can enhance machine learning algorithms. This course introduces the core concepts of quantum mechanics and the necessary mathematical tools. Exposure to topics such as complex numbers, matrices, calculus, and vector algebra helps in understanding the mathematical formulations of quantum machine learning models. In addition, the introduction to Dirac notation and the Schrodinger equation builds familiarity with the language of quantum mechanics. Prospective Quantum Machine Learning Researchers should take this course to gain a baseline in the required fundamental knowledge.

See salaries and explore the career path for Quantum Machine Learning Researcher

Quantum Algorithm Developer

A Quantum Algorithm Developer designs and implements quantum algorithms to solve specific computational problems. This course helps lay a foundation in the essential principles of quantum mechanics. The topics covered such as complex numbers, matrices, calculus, and vector algebra directly apply to the mathematical formulations used in algorithm design. Furthermore, understanding Dirac notation and the Schrodinger equation, a part of the material, may offer insights into manipulating quantum states. Aspiring Quantum Algorithm Developers should take this course to establish a baseline understanding of the underlying mathematical and quantum mechanical concepts.

See salaries and explore the career path for Quantum Algorithm Developer

Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models. The course may be useful to gain insight into quantum machine learning, an emerging field which may revolutionize this role. By understanding the foundations of quantum computing as well as the introduction to the Qiskit library, one may be able to experiment with the latest tools. A Machine Learning Engineer should take this course to build knowledge of the intersection of machine learning and quantum computing.

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Quantum Software Engineer

A Quantum Software Engineer builds software tools and libraries to support quantum computing platforms. This course may be useful because it provides a grounding in the quantum mechanics that are relevant to quantum computing. The quantum computing series will begin Qiskit programming, which is relevant for this role. Quantum Software Engineers should take this course to begin their journey understanding the bridge between theoretical quantum concepts and practical software implementation.

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Data Scientist

Data Scientists analyze complex data sets, create models, and communicate insights to drive business decisions. This course may be useful because it introduces quantum computing concepts that are becoming increasingly relevant to advanced data analysis techniques. While quantum computing is not yet mainstream in data science, understanding its principles might unlock new approaches to data processing and algorithm design. A Data Scientist should take this course to explore emerging possibilities at the intersection of quantum computing and data science.

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Artificial Intelligence Engineer

An Artificial Intelligence Engineer designs, develops, and deploys AI models and systems. This course introduces the foundational concepts of quantum computing and quantum machine learning. The quantum computing course can help one explore theoretical relationships between the disciplines. An Artificial Intelligence Engineer should take this course to understand the potential of quantum computing in advancing artificial intelligence.

See salaries and explore the career path for Artificial Intelligence Engineer

Physics Professor

A Physics Professor teaches physics courses at the college or university level and conducts research in physics. This course helps one review the fundamentals of quantum mechanics and quantum computing. The course may be useful for refreshing knowledge of mathematical concepts. Aspiring Physics Professors should take this course to solidify their understanding of quantum mechanics and stay current with advancements in quantum computing.

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Cloud Solutions Architect

A Cloud Solutions Architect designs and implements cloud computing solutions for organizations. This course may be useful in understanding how quantum computing resources may be integrated into cloud infrastructures in the future. Understanding the pre requisites in this series may help the architect design cloud applications. One interested in being a cloud solutions architect should take this course to keep abreast of emerging technologies and their potential impact on cloud computing architectures.

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Cryptography Engineer

A Cryptography Engineer focuses on developing and implementing secure communication methods, often focusing on encryption and decryption algorithms. This course may be useful for understanding how quantum computing can break existing cryptographic systems and how quantum cryptography offers new secure solutions. The course may help one understand quantum mechanics and mathematical foundations. A Cryptography Engineer should take this course to explore new cryptographic paradigms to stay competitive in the field.

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Technical Writer

A Technical Writer creates documentation such as user manuals, guides, and white papers. This course may be useful for developing a baseline understanding of quantum computing and quantum machine learning, which is relevant for writing technical content in these areas. By taking this course, a technical writer would have insight into quantum mechanics and mathematical foundations. A Technical Writer should take this course to gain the knowledge required to produce high-quality technical documentation.

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Financial Analyst

A Financial Analyst analyzes financial data, provides investment recommendations, and manages financial risk. While seemingly distant, the course may have uses. Quantum computing has the potential to revolutionize financial modeling and risk analysis; this course may lay a foundational understanding of the mathematics behind it. A Financial Analyst should consider this course to explore how quantum computing might transform the financial industry in the future.

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Educator

An Educator works to teach students in the K-12 grades. The course may be useful to learn about the latest advances in quantum computing and quantum machine learning. By taking this course, an educator can broaden their horizons and create innovative lesson plans. The Educator may become more prepared to teach new material.

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Systems Engineer

A Systems Engineer designs, integrates, and manages complex systems, ensuring that all components work together effectively. This course may be useful for understanding how quantum computers could be integrated into larger systems in the future. It may also provide background for specialized quantum systems design. Systems Engineers should take this course to broaden their awareness of emerging technologies and their architectural implications.

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Quantum Computing and Quantum Machine Learning - Part 1

What's inside

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Syllabus

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