#874 overall
Quantum Computation and Information (Ryan O'Donnell)
Carnegie Mellon University
1 recommender · 1 thread · recommended in 1 year, 2020 · score 0.7
A Carnegie Mellon University course taught by Ryan O'Donnell covering quantum computation and information, including the capabilities of quantum computing, its connections to Fourier analysis and algorithms, and quantum complexity theory. It is delivered as a recorded lecture playlist.
Commenters call it the best option for those who want to really understand what quantum computing can do, with practice in the underlying math and intuition for multi-qubit systems.
Science & Engineeringintermediatefreevideo lectures#quantum computing#quantum information#quantum complexity#fourier analysis#o'donnell
Recommendations by year
20062026
Learning Quantum Computing based on skill level (math is the biggest friction point, suggested pdf should save you lots of time) (Recc is personal recommendations, Hi-Recc is look at ASAP)
# QC Main Ideas
- Rotate, Compute, Rotate - Think in Amplitude Interference
# Beginner:
-(Hi-Recc) Quantum Computing Primer (1.5hr) : https://www.youtube.com/watch?v=F_Riqjdh2oM -(Hi-Recc) Math Primer for Quantum Computing (easiest intro/primer I found on the topic; Highly Recommend ) : https://cds.cern.ch/record/1522001/files/978-1-4614-6336-8_BookBackMatter.pdf -- understand Bra Ket notation [<Bra|Ket>] (Ket as Column vector, Bra (Row vector) as Complex Conjugate of Ket (denoted as dagger) ) -- understand Kronecker product ( for multi-qubit systems) - Quantum Computing for Computer Scientists book - https://www.amazon.com/Quantum-Computing-Computer-Scientists-Yanofsky/dp/0521879965 - Quantum Math Primer (Faculty of Khan) (found a bit hard the first time around, pretty dense) : https://www.youtube.com/playlist?list=PLdgVBOaXkb9AtG88OsK_c8FDEBDLCC6_9
# Intermediate
-(Recc) Ryan O'Donnell CMU course [is the best if you want to really understand the capabilities of quantum computing, get practice with math, intuition] (algos connection to Fourier, Quantum Complexity Theory, Math best practices, learning multi-quibit systems) -- Quantum Computation and Information at CMU : https://www.youtube.com/playlist?list=PLm3J0oaFux3YL5qLskC6xQ24JpMwOAeJz -- Lecture Notes (use as reference in case video is not clear, or camera shot lags/changes) https://www.cs.cmu.edu/~odonnell/quantum18/ - Mermin's Textbook https://www.goodreads.com/book/show/1959623.Quantum_Computer_Science - Nielsen & Chuang's Textbook https://www.amazon.com/Quantum-Computation-Information-10th-Anniversary/dp/1107002176 -- Nielsen's Lectures https://www.youtube.com/playlist?list=PL1826E60FD05B44E4
# Advanced
-(Recc) Scott Aaronson Graduate Course http://stellar.mit.edu/S/course/6/fa14/6.845/materials.html -(Recc) Scott Aaronson Papers (really interesting) https://scottaaronson.com/papers/ - Complexity Zoo - List of Algorithms https://complexityzoo.uwaterloo.ca/Complexity_Zoo -(Recc) Machine Learning https://www.amazon.com/Quantum-Machine-Learning-Computing-Mining/dp/0128100400
# Reference:
-(Recc) https://qiskit.org/textbook/preface.html ToC for different algorithms ( easy to follow, do it for quick basic algo math implementation lookup) - 'Suggested texts, notes, and videos to look at' section at bottom of page https://www.cs.cmu.edu/~odonnell/quantum18/
( I found this skill level format useful when learning Haskell/Functional Programming Paradigm. This is what I found useful for getting started with minimal friction; if more of a textbook learner Nielsen/Chuang textbook or Quantum Computing for Computer Scientist's)