Course Details

Pros: 1. Unique course that tackles the cutting edge of computer science 2. Highly mathematical with interesting subjects ranging from quantum physics to abstract algebra to proofs If you're into that 3. All the course material is available from the beginning so you can go as fast as you want Cons: 1. Very limited application outside of theoretical subjects at the moment 2. Time consuming homeworks 3. Exams are very tricky and hard to prepare for unless you have had a graph theory/abstract algebra background Detailed Review: Overall, a really nice course- the lectures by professor Aaronson are nice too. The entire course material for the spring 2023 was based off of a PDF you can find online. I will say the exams were very tricky and required some insight into the problem and a thorough understanding of the algorithm that would be changed slightly to fit the exam problem. So the advice I would give is to read the book but also try out different variations of the algorithms and ask about them in the discussion forms. TA support was excellent

Pros: 1. Very interesting content that is explained well in lectures. 2. Highly active and helpful TAs. 3. After the maths foundations are set, the course is much more about developing algorithms to solve problems than doing maths. Cons: 1. Peer grading is an extra time cost and can be hit-and-miss, but you can get regrades from the TAs if needed. 2. 3. Detailed Review: The course was surprisingly not too maths heavy imo (but I have a maths background, so...). The first few weeks focus on concepts like complex numbers and unitary matrices because you need them to understand quantum states, but at the halfway mark you should have all the maths needed to understand quantum states/calculations in general. You do end up needing some number theory for Shor's algorithm, but they walk through the relevant stuff and I don't think it's a focus (just needed to understand that understand/appreciate the algorithm). It sounds like the course is relatively unchanged from the first run.

Pros: 1. Amazing professor and lectures 2. Very good lecture summary notes 3. Great recitation and homework problems Cons: 1. Homeworks are extremely hard 2. Office hours are limited compared to the content 3. Hard exams with no curve Detailed Review: This course is an amazing course on the topic of basic Quantum mechanics and computing. Professor Aaronson is simply an amazing Professor who explains the concepts very well. The concepts themselves are difficult to grasp for first-timers specially after the midterm, but the professor still does a great job. The TAs are also very knowledgable as they are PhD students in the Quantum field. Homeworks and recitation notes enforce learning and the "textbook" helps to explain key points. The issue that some may face is how math heavy this course is. I personally had math background but still needed to refresh as the concepts require math knowledge. Most importantly you should to be comfortable with Linear Algebra, probability, complex numbers, and some computer complexity concepts. The homeworks take a lot of time as they are very difficult. You need hours of office hours and restudying to understand and solve the problems. There are some recitation problems to help you, but other than that you're on your own. Every week, there's a new homework and it becomes a marathon. Also due to the number of students, the office hours are always packed with people jumping over your questions. This is expected for a difficult online course, and this course would definitely be better in person. The final note is that the two exams (midterm and final) are difficult. They are a bit easier than the homeworks, but the time is limited. You need some creativity and a deep knowledge to be able to get a good grade. Also, there is no curve, meaning that if you get a grade, that is your final grade. That's where this class becomes stressful and a bit tough compared to other courses in this program. With all that being said, I really enjoyed this learning experience, and regardless of the grade I got, I feel like I learned a lot more than some other classes which I got an A in. Definitely one of the best courses in this program if not the best one. You should definitely take it if you are interested!

Pros: 1. I definitely know what quantum computers are now. 2. Class elegantly builds up from the basics (quantum states) to Shor's algorithm, covering pretty much all the details of how it could be implemented, hypothetically. 3. Lots of engaging math, you will work to apply different kinds of linear algebra, proofs by reduction, information science principles in situations where it's really not obvious what the results will be. You'll come away feeling like you really figured some stuff out. Cons: 1. It's a demanding class. Some of the homeworks require creativity in proving things that are totally not obvious; even going to office hours and seeking out 'hints' sometimes I spent crazy amounts of time to (mostly) finish the homework. Hardest class I've taken in the program by quite a bit, although it was basically an un-altered undergraduate honors class so it's hard to argue that they need to make it easier. 2. Peer grading (it is what it is, again this encourages actually understanding the material. 3. Lots of work on arcane sub-topics that, unless you end up being a quantum scientist, you will never think about again. Detailed Review: Class is excellent, and the work load led me to understand the material in a much deeper way than I otherwise would have. But it was a lot. Took this along with a super easy course + full time work - it was a slog of a semester, but kind of awesome. Final was pretty difficult and long - no trick questions, but not trivia either. "Textbook" by the professor closely mirrors the course, makes the homeworks a lot easier - don't forget it's there!

Pros: 1. Excellent lectures are engaging and thought-provoking 2. Responsive TAs 3. Recitations are helpful with assignments 4. Fair and transparent grading policies Cons: 1. Low grade distribution and high drop rate 2. Timed exams worth 50% of the overall grade 3. Excessive time commitment just to get a B 4. Occasional conflicting guidance from TAs 5. Overcrowded office hours and underutilization of Piazza Detailed Review: The class is a struggle for anyone taking a quantum course for the first time. However, some students have taken multiple quantum courses prior on Coursera, and a few dozen students drop the course before the first midterm as the content monotonically increases in difficulty and complexity; therefore, the final grade distribution is somewhat inflated. The true average in this course is a B- and the course load required to achieve such a grade is significant. There is no curve, exam extra credit is overly challenging, and participation extra credit should not be assumed. While it is possible to succeed in this course without prior knowledge, it is okay to assume ~15% of students will make an A and the average time commitment is at least 20 hours a week. My main issues with the course are the timed exams and the ineffectiveness of office hours. Exams are timed and do not allow students time to think of the creative, algorithmic solutions that homework problems often require. While homework sets do require students to come up with algorithms to solve complex problems and proofs, there was really no way to prepare to derive these algorithms in 5 minutes on timed exams, making it difficult for most students to get an A. Office hours are generally helpful but are too open-ended to be as effective as they could be. It would be much better if the TAs coordinated an agenda to facilitate understanding lecture topics and homework sets. Instead, the time is not used effectively since many students do not watch the office hours recordings and instead repeat questions in every session. The TAs try to accommodate everyone at the cost of efficiency while students underutilize Piazza as a resource to get questions answered. Office hours are optional, but I found them necessary to understand the homework sets, but attending the sessions each week did add a considerable amount to the time commitment for this course. Overall, it's an interesting course that could be improved with better use of office hours and simply curving the course.

Pros: 1. Great lectures 2. Professor occasionally responds to Piazza posts and did a special Q&A at the end of the quarter 3. Lead TA was heavily involved in the class and very responsive (seemingly at all times, pretty much 24/7) Cons: 1. Weekly HWs that are usually very time-intensive; little to no break once this rollercoaster starts 2. Multiple choice exams with little to no partial credit, so it's easy to get a mediocre score even if you studied for days 3. Don't take this class expecting a high/good grade, even if you are used to getting A's (i.e., don't be obsessed with grades if you are taking this class!) That said, the grading is fair. You get what you earn, nothing more. Detailed Review: Great class from a leading expert in the field of quantum computing. This is a theory-heavy class, so if you are coming from an applied science or engineering background, you may struggle (like I did) on the fact that the class focuses a lot on theorems and proofs. The current state of the art and industry achievements are mostly left to the last lecture, but this class is a great place to get started to really understand the theory underlying all of QC applications. It is definitely worth it to understand these principles. Aaronson's lecture notes are extremely useful and a great read, even if you are not enrolled in this class / don't plan to take this class. Warning: The first two HWs are deceptively easy; this peace doesn't last long. I wish I had known in advance that I was going to have restless nights and the fear of failing constantly looming over me once I hit the fourth week of this class. Don't take this class if you have a very difficult, time-consuming full-time job or other responsibilities that may get in the way of studying. This class will demand your full devotion every week, no breaks. Be prepared for what you are signing up for and you won't be disappointed.

Pros: 1. Very engaging and interesting lectures 2. Homework that enhanced the understanding of the course concepts 3. Helpful TAs Cons: 1.Heavy workload 2.Restrictive Office Hours 3.Imperfect Exams Detailed Review: Taking this course was a very enjoyable while simultaneously painful experience. The lectures are well-done and I found the course material extremely engaging. Professor Aaronson is clearly a leading expert in this field, but thankfully, also a gifted lecturer, able to explain these concepts to others. The TAs also spent a lot of time answering questions and being as helpful as possible. As I'll detail below, at times I felt they were hamstrung by the nature of the HW problems, but all the TAs to a person were as helpful as they could be and would regularly spend more time than required of them to help students. The homework really helped to understand course concepts but was extremely time consuming. A lot of the homework was not simply applying a concept from lecture, but usually required proofs and problem solving that are tricky to say the least, and built upon course concepts. This made the amount of time spent extremely variable and wasn't as simple as re-watching the lectures or re-reading the course notes. At times I'd breeze through problems others were struggling with in TA sessions, other times I'd find myself not seeing the "trick" to solve a problem that others did. This also made it difficult for TAs to provide useful assistance on HW. If the core of solving a problem involves a "trick" or novel approach, giving that away, gives the solution to the problem, but anything other than that doesn't tend to be very helpful for the students. I lost track of the amount of times I'd lose hours just staring at a problem trying to find another approach. I understand why they did this, they are trying to build intuition for concepts that aren't naturally intuitive, but it's a painful process. That being said, when you solved the HW and got a good grade, you can be confident you understood the concepts and feel a great sense of accomplishment. This homework is weekly, so the grind really starts to weigh on you week after week with very little reprieve except for the mid semester break (Spring Break). Once one HW is turned in you should really be starting on the next one as soon as possible. As someone working full-time in the Central Time Zone I found it very hard to attend TA sessions. Almost all sessions were in the middle of the workday, making it hard to attend, and no sessions on the weekend at all. In future iterations of the class it'd be nice to have some weekend sessions when those of us who are working full-time are mostly likely to be engaging with the course material. The TAs did try to mitigate this by recording lots of sessions, but it's not the same as being able to ask questions yourself. The last negative were the exams. They make up 50% of the grade, are very long, and offer no significant partial credit. On the midterm I found myself barely able to finish on-time and lost numerous points for careless arithmetic mistakes due to the speed I was working. The final was longer, and we had more time, which was nice, but some of the exam problems required applying QIS concepts to algorithmic problems I wasn't familiar with. For example, the practice final involved using QIS to solve Graph Ismorphism, assuming the students were already familiar with those algorithms, which even though I received my undergrad in CS from UT Austin, I was not (it's been over a decade since my undegrad). Ultimately, this points a little bit of a trend I've seen with this program, which is that the courses are really geared towards students who are coming right from undergrad and have a lot of concepts at the ready, not people who are going back to school after time away from the core undergrad curriculum. If that sounds like you, just be prepared, brush up on your linear algebra and peruse the course lecture notes ahead of time if you can. It might sound like I'm a bit negative on this course but I really loved it. It's currently my favorite course I've taken in the program. If you are willing to spend A LOT of time on this class, it will be deeply rewarding, and the course resources are there for you to succeed. Just go into this course with 1) an interest in the subject and 2) an ability/willingness to put the time in to master the concepts. If you are taking this class with another course in a given semester, ensure the other one has a much lower workload or you're asking for trouble.

Pros: 1. Rigorous intro to underlying concepts of quantum information, learning from one of the leading figures in the field 2. TAs are highly responsive and good at breaking down lecture concepts 3. I feel I know enough to begin understanding recent literature in the QIS space Cons: 1. [Arguable] Homeworks are proof-heavy and can consume quite a few hours; they tend to be extensions of the lecture material as opposed to repetitions of what we covered in class 2. Grading can be stingy. Borderline scores weren't bumped up to the next grade 3. Midterm and final exams are long: 3-4 hours of multiple choice and short-answer questions Detailed Review: Overall, I think this is one of the best electives in the MSCSO program despite being a new course offering (Spring 2022). Prof. Aaronson was not active in day-to-day course management during the semester, but his lecture videos and notes were concise and easy to follow. The homeworks (30% of grade) were challenging. From a learning perspective, I found this to be a good thing because the proof and algorithm questions helped reinforce what we covered in class; I have a better grasp of the "edges and borders" of the problems and theorems we went through because of these assignments. Prepare to spend up to 10-12 hours per week on them though, while also attending office hours / sleuthing on Piazza. The greatest point of contention in this class was the grade distribution. Our midterm average and median were squarely in the 'C' range, and a vocal portion of students (I assume to be a minority) insisted on having the class curved -- this was not guaranteed in the originally syllabus FWIW. It's true that the grade outcomes were lower than for a typical MSCSO course, but the course itself had already been adapted from the undergraduate version (and with more lecture chapters omitted). Although I would've benefited from even a 0.5% bump, I could respect the prof's decision on a no-curve because one would expect graduate students to be held to a higher bar.

Pros: 1. Lectures are short, interesting, and to the point 2. Concise and sufficient notes as required reading (i.e. the "textbook") 3. Teaches the nuts and bolts of QC/QM, which not all courses do Cons: 1. Homeworks take a considerable amount of time and effort 2. Not being great in math or proofs made this class difficult for me 3. OH are usually crowded and may be difficult to get your questions in Detailed Review: I joined UT specifically because no other online program I knew of had a QIS class. Prof. Aaronson speaks at a lot of events, but none of his lectures are available online. Having now taken this class I'm thoroughly impressed with the quality of the material and the teaching ability of Prof. Aaronson. The TAs were exceptional in their support. If you're wondering how to budget time, this was my breakdown per week: - W1: 8.75 hours [1.5 hours (readings); 3 hours(lectures); 4.25 hours (hw wk2)] - W2: 8.75 hours [3 hours (hw wk2); 2.25 hours (lectures); 1 hour (OH); 2.5 hours (recitation wk2)] - W3: 10.75 hours [2.5 hours (lectures); 3.5 hours (recitation wk3); 1 hour (peer-grading hw wk2); 1 hour (readings); 2.75 hours (hw wk3)] - W4: 15 hours [1 hour (hw wk3); 5.25 hours (lectures); 1.75 hours (recitation wk4); 4.75 hours (hw wk4); 1 hour (OH); 0.5 hour (readings); 0.75 hours (peer-grading hw wk3)] - W5: 3 hours [1 hour (OH); 1 hour (wk5 recitation); 1 hour (hw wk5)] - W6: 17 hours [2 hours (OH); 5.5 hours (hw wk5); 1 hour (peer-grading hw wk4); 3.25 hours (lectures); 0.75 hour (recitation wk6); 2 hours (readings); 2.5 hours (hw wk6)] - W7: 24.5 hours [1.25 hours (OH); 7.5 hours (hw wk6); 0.5 hour (peer-grading hw wk5); 3.25 hours (lectures); 1 hour (readings); 7 hours (recitation wk7); 4 hours (hw wk7)] - W8: 21.25 hours [6.25 hours (OH); 6.5 hours (hw wk7); 0.75 hour (peer-grading hw wk6); 4 hours (midterm prep); 0.25 hour (peer-grading hw wk7); 2.5 hours (midterm); 1 hour lectures] - Spring Break: 4.25 hours [2.75 hours (lectures); 1.5 hours (readings)] - W9: 8 hours [2.25 hours (lectures); 0.5 hour (readings); 1.75 hours (recitation wk9); 3.5 hours (hw wk9)] - W10: 10 hours [2 hours (OH); 5.75 hours (lectures); 1.5 hours (recitation wk10); 0.75 hour (readings)] - W11: 20 hours [1.75 hours (OH); 6.5 hours (hw wk10); 3.25 hours (lectures); 0.75 hour (peer-grading hw wk9); 0.5 (peer-grading hw wk10); 2.25 hours (recitation wk11); 5 hours (hw wk11)] - W12: 11.5 hours [1.5 hours (OH); 0.5 hours (hw wk11); 3.5 hours (lectures); 2 hours (recitation wk12); 4 hours (hw wk12)] - W13: 12.5 hours [7 hours (hw wk12); 1 hour (OH); 0.5 hour (peer-grading hw wk11); 3.25 hours (lectures); 0.75 hour (recitation wk13)] - W14: 15 hours [3.5 hours (hw wk13); 1 hour (OH); 1 hour (peer-grading hw wk12); 3.25 hours (lectures); 4 hours (readings); 0.25 hour (recitation wk 14); 0.5 hour (peer-grading hw wk13); 1.5 hours (hw wk14)] - W15: 6 hours [0.25 hour (peer-grading hw wk14); 5.75 hours (final prep)] - W16: 8.5 hours [4.5 hours (final prep); 4 hours (final)]