A simple conceptual introduction to quantum mechanics and quantum computation.
With an apprenticeship you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.With this course you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.
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Online
Start date
Different dates availableEnrolment now open
About this course
A strong background in basic linear algebra, including vectors, matrices, complex numbers, inner products, eigenvalues and eigenvectors (a simple diagnostic quiz will help you assess your background). Mathematical maturity and familiarity with ideas of computer science such as big-Oh notation, algorithms and how to bound the running time of an elementary algorithm.
Once you have registered, you can access our simple optional diagnostic quiz and based on your answers you will be pointed to online resources that you can use to brush up on your background knowledge.
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This centre's achievements
2017
All courses are up to date
The average rating is higher than 3.7
More than 50 reviews in the last 12 months
This centre has featured on Emagister for 8 years
Subjects
Mechanics
Quantum Mechanics
Quantum
Computer Science
Algorithms
Course programme
Quantum computation is a remarkable subject building on the great computational discovery that computers based on quantum mechanics are exponentially powerful. This course aims to make this cutting-edge material broadly accessible to undergraduate students, including computer science majors who do not have any prior exposure to quantum mechanics. The course starts with a simple introduction to the fundamental principles of quantum mechanics using the concepts of qubits (or quantum bits) and quantum gates. This treatment emphasizes the paradoxical nature of the subject, including entanglement, non-local correlations, the no-cloning theorem and quantum teleportation. The course covers the fundamentals of quantum algorithms, including the quantum fourier transform, period finding, Shor's quantum algorithm for factoring integers, as well as the prospects for quantum algorithms for NP-complete problems. It also discusses the basic ideas behind the experimental realization of quantum computers, including the prospects for adiabatic quantum optimization and the D-Wave controversy. Before your course starts, try the new edX Demo where you can explore the fun, interactive learning environment and virtual labs. Learn more. Do I need a textbook for this class? No. Notes will be posted each week. If you wish to consult other references, a list of related textbooks and online resources will be provided. What is the estimated effort for course? About 5-12 hrs/week. Why is the work load range so wide? How long you spend on the course depends upon your background and on the depth to which you wish to understand the material. The topics in this course are quite open ended, and will be presented so you can understand them at a high level or can try to follow it at a sophisticated level with the help of the posted notes. How much does it cost to take the course? Nothing! The course is free. Will the text of the lectures be available? Yes. All of our lectures will have transcripts synced to the videos. Do I need to watch the lectures live? No. You can watch the lectures at your leisure.
Additional information
Umesh V. Vazirani Umesh Vazirani is the Strauch Distinguished Professor of Electrical Engineering and Computer Science at University of California, Berkeley, and is the director of the Berkeley Quantum Information and Computation Center. Professor Vazirani has done foundational work on the computational foundations of randomness, algorithms and novel models of computation. His 1993 paper with Ethan Bernstein helped launch the field of quantum complexity theory. In 2007-08, he was appointed Keenan Visiting Professor for distinguished teaching at Princeton University. He is the author of two books An Introduction to Computational Learning Theory with Michael Kearns (MIT Press) and Algorithms with Sanjoy Dasgupta and Christos Papadimitriou (McGraw Hill).