Quantum Computing is fast becoming a popular phenomenon in the world of physics, mathematics, and science. If you are one of those who are interested in understanding quantum computing or if you have already made the decision to venture into quantum computing as a career, the below list of courses will prove a great place to start on your journey to understanding quantum computing.
Practical Python for AI Coding 1
The Korea Advanced Institute of Science and Technology (KAIST) offers Practical Python for AI Coding available online on Coursera. In this course, popular Python libraries, functions, and syntaxes used in AI development are selected, introduced, and explained. This course is beneficial for even seasoned Python users since it teaches crucial syntaxes and functions that are frequently used in AI coding and illustrates how NumPy, Pandas, and TensorFlow work together as a unit.
This course is extremely beneficial to anyone in the field of quantum computing. There are many Python packages for quantum computing. The main benefit of learning Python for programming quantum computers is the sheer number of Python-based software packages available for simulating or communicating with quantum computers.
Introduction to Quantum Information
Just like the first course on our list, this course is offered by The Korea Advanced Institute of Science and Technology (KAIST) and is available on Coursera. With Introduction to Quantum Information learners can gain foundational knowledge of quantum computing. It focuses on the basic knowledge of how quantum systems process information and how quantum features relate to computing and communication tasks. As the foundation for information processing, quantum theory is introduced at the beginning of the course. Single and dual qubit quantum systems are introduced. The preparation, evolution, and reading of qubits are used to explain quantum theory axioms such states, dynamics, and measurements. It explains quantum communication and computing.
One essential tool for processing quantum information is entanglement, which offers entangled state manipulation and quantification. Some skills enrollees can expect to gain include Computational Logic, Theoretical Computer Science, Algorithms, Computer Architecture, Hardware Design, Linear Algebra, Mathematics, Cloud Computing, Computer Vision, Machine Learning, Microsoft Azure, and more.
Math Prerequisites for Quantum Computing
The Math Prerequisites for Quantum Computing course is available to potential learners on Udemy and is offered by Kumaresan Ramanathan, and has a 4.7 star rating from over 700 students. The course includes six core sections including Boolean Algebra, Cryptography, Probabilty, Statistics, Complex Numbers, and Linear Algebra & Matrices.
Linear algebra in various forms can be used to describe everything in quantum computing, from the representation of qubits and gates to the operation of circuits. Basic probability theory is another area of mathematics that is heavily entwined with quantum computing, therefore both linear algebra and probability theory, as well as other fields of math are crucial to comprehending quantum computing.
Quantum Physics For Quantum Computing
The Quantum physics for Quantum Computing course is available to potential learners on Udemy and is offered by Kumaresan Ramanathan, and has a 4.6 star rating from 400 students. The course includes six core sections excluding the introduction and conclusion. They are Polarization of Light, Quantum Behaviour of Polarizers, Information in Quantum Systems, Quantum Measurment, Single Particle Systems – Superposition and Measurment, and Two Particle Systems – Entanglement and Bell States.
Quantum computing focuses on the principles of quantum theory, which deals with contemporary physics that explain the behavior of matter and energy of an atomic and subatomic level. Quantum computing makes use of quantum phenomena, such as quantum bits, superposition, and entanglement to perform data operations. In order to understand quantum computing, one must first have an understanding of physics.
Advanced Math for Quantum Computing
The Advanced Math for Quantum Computing course is available on Udemy and is offered by Kumaresan Ramanathan. This course takes the learner a step further into the mathematics that they need to know in order to take on quantum computing. It is advised for learners to not just take basic math, but also advanced math to properly understand the application of mathematics to quantum physics. This course covers the Math the learner needs to begin learning about quantum algorithms and applications of quantum computing.
This course covers several math techniques including Orthonormality, Basis Vectors & Change of Basis, Bloch Sphere, Tensor Products, Multi-Qubit Tensor Algebra, Entanglement in terms of Degrees of Freedom, Partial Measurements, Cryptography with Entanglement, and, Deconstruction of Hermitian and Unitary Matrices into a Sum of Outer Products. As well as some quantum applications including Superdense Coding, Quantum Teleportation, Proof of No-Cloning Theorem, and, Bell’s Theorem (Statement and Proof).
The Quantum Internet and Quantum Computers: Will They Change the World?
Available on Edx, The Quantum Internet and Quantum Computers: How Will They Change the World? is offered by Delft University of Technology as a self-paced course that can be completed over six weeks. The course provides learners with a basic understanding of quantum computing and the quantum internet. Learners will learn the key application areas in which quantum technologies will change the world, the potential advantages of a quantum computer and the quantum internet, but also the challenges in realizing them, and the basic quantum phenomena that make quantum technologies possible.
The course is available for free to learners, however free learners will forego a certificate when they choose this option. Instructors include Stephanie Wehner, Professor, QuTech, Delft University of
Technology; Lieven Vandersypen, Professor, QuTech, Delft University of Technology; Menno Veldhorst Tenure Track Team leader and Roadmap leader at QuTech. Delft University of Technology.
Quantum Machine Learning
Also available on Edx, the Quantum Machine Learning course is offered by the University of Toronto. Learners will learn to Distinguish between quantum computing paradigms relevant for machine learning, Assess expectations for quantum devices on various time scales, Identify opportunities in machine learning for using quantum resources, and to Implement learning algorithms on quantum computers in Python.
The field of quantum computing greatly benefits from the application of machine learning. Quantum machine learning makes use of qubits, quantum processes, or specific quantum systems to speed up computing and data storage performed by algorithms in a programme whereas ordinary machine learning methods are used to calculate enormous amounts of data.
The Complete Quantum Computing Course
The Complete Quantum Computing Course is available to potential learners on Udemy and is offered by Codestar, and has a 4.4 star rating from over 900 students. The course includes nine core sections excluding the introduction and next steps sections. They are Mathematical Foundations, Qubit and Physics, Python from Scratch, Qiskit 101, Teleportation, Bernstein Vazirani, Deutsch, Grover’s, and Shor’s.
The course is taught by Atil Samancioglu, who has more than 250.000 students worldwide on Programming & Cyber Security along with the Codestars, serving more than 1 million students online. Atil is co-founder of Academy Club & Pera Games and he also teaches programming in Bogazici University in Turkey.
MIT Quantum Computing Fudamentals XPro
MIT offers the Quantum Computing Fundamentals course online. For $2,249 learners will learn to Describe the differences between quantum and classical computation, Discern potential performance gains of quantum vs. classical algorithms, Assess the business applications of quantum computation, Understand engineering challenges currently faced by developers of quantum computers, Become proficient with engineering requirements for quantum vs classical algorithm implementation, Discern the scientific limits of quantum algorithms for chemistry and optimization, Determine technical requirements for quantum computers to run realistically large quantum algorithms, Evaluate key technology requirements for quantum computers to be able to function properly, and to Understand the mathematical description of quantum states and basic quantum operations.
The course lasts for four weeks and is taught by facaulty from MIT including Isaac Chuang, Professor of Physics, Professor of Elecrical Engineering, and Senior Associate Dean of Digital Learning at MIT; William D. Oliver, Ph.D. ,Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science
Professor of Physics, Laboratory Fellow, Lincoln Laboratory, Director, Center for Quantum Engineering, and Associate Director, Research Laboratory of Electronics; among others.
Understanding Quantum Computers
The Understanding Quantum Computers is a four-week online course offered by Keio University on FutureLearn. The course is designed as an introductory course to teach the key concepts of quantum computing and how it is changing computer science.
Topics included in the course are Waves and interference, Quantum superposition and entanglement, Computational complexity, The quantum Fourier transform, Shor’s algorithm for factoring large numbers, Grover’s algorithm, Quantum chemistry and machine learning, Physical phenomena as quantum bits (qubits), Quantum computing hardware and architecture, Quantum error correction, and The quantum information technology industry.
At a predicted CAGR of 31.2% during the projection period, the quantum computing market is expected to increase from $712.2 million in 2022 to $4,758.0 million in 2029. With the industry growing as fast as it is, the need for more people to understand quantum computing whether as participants in the industry’s workforce or as partakers of the products of quantum computing across other industries.