COMPSCI 490Q: Quantum Information Science

Organization

Course number: COMPSCI 490Q
Class hours: Monday, Wednesday: 10:10am - 11:25am
Class location: Computer Science Building 140
Instructor: Filip Rozpedek
- Office: A211E LGRC
- E-mail: frozpedek[at]umass.edu
- Office hours: 11:30am-12pm Monday, Wednesday
TA: Snehasis Addy
- E-mail: saddy[at]umass.edu
- Office hours: 3pm - 4pm Tuesday
- Office hours location: LGRT T222
Undergraduate TA: Minh Do
- Email: mhdo[at]umass.edu
- Office hours: 5:30pm-6:30pm Wednesday
- Office hours location: LGRT 220
Undergraduate TA: Jocelyn Velazquez
- Email: jvelazquez[at]umass.edu
- Office hours: 5:30pm-6:30pm Wednesday
- Office hours location: LGRT 220

(Note: Please include COMPSCI 490Q in your email subject line for class related stuff!)

Quantum information science (QIS) revolutionizes our understanding of the fundamental laws of the universe and promises world-altering improvements in a number of practical computational tasks. For theoretical computer scientists, QIS provides the means to unlock the ultimate computational powers available to us in this universe. For programmers and computer engineers, QIS offers the tools to run simulations and optimizations otherwise infeasible on classical computers. For theoretical physicists, QIS gives us hope of resolving paradoxes foundational to our understanding of Nature. And for experimentalists and engineers, QIS also enables the creation of exquisite sensors and novel communication tools, far outperforming what is classically permitted.

This class will introduce the notion of quantum probability amplitudes, i.e., the “correct” probabilistic description of Nature, and describe how these quantum phenomena permit the creation of new types of computational and communication machines. The introduction to foundational quantum information science will be followed by a few practical (and impractical) quantum algorithms, illustrating the counterintuitive computational powers of quantum mechanics. The latter half of the class will focus on the difficulties of creating such extremely fragile computational machines in our noisy and unforgiving real world.

Previous versions of the class (taught by Stefan Krastanov): 2024.

Prerequisites

You truly need to be knowledgeable in Probability Theory and Linear Algebra. However, no Calculus is necessary.

A grade of C or better in each of:

MATH 132 Calculus II
MATH 235 Introduction to Linear Algebra
one of COMPSCI 240 or STAT 515 or PHYSICS 281 or PHYSICS 287

These classes could be helpful but are not necessary:

MATH 233 Multivariate Calculus
COMPSCI 250 Introduction to Computations
COMPSCI 311 Introduction to Algorithms

Submit an override form if you can not directly signup through SPIRE.

Learning Objectives

Understanding of classification of deterministic, probabilistic, and quantum algorithms, in particular the difference between classical probability and quantum probability amplitude;
Familiarity with the “killer apps” for quantum computing and networking hardware, where they have capabilities beyond those of classical computers;
Understanding of the limitations of quantum computers: in what situations are they not more powerful than classical computers;
Modeling of noisy quantum hardware and standard error correction techniques permitting the creation of reliable quantum hardware out of noisy unreliable quantum systems.

Textbooks

A variety of materials will be provided as the class progresses and each lecture will have suggested readings from multiple sources. You can see a preview of the most important sources (provided by Stefan Krastanov).

The majority of the class will be based on Scott Aaronson’s lecture notes. The Nielsen & Chuang textbook would be an important supplementary resource and a source of practice problems. See the link above for both.

Onboarding Exercises

There are optional ungraded exercises (provided by Stefan Krastanov) you can use to practice. Office hours would be a good place to discuss them.

Calendar

26 class days

Exams:

Midterm, take home exam, 7am-11:59pm, week of October 14th
Final, in-person written exam, date TBD

Take home exam is completely open book and open internet, but no communication with other sentient beings is permitted (e.g., yes to using search engines, no to asking new questions on forums, no to working with classmates).

The final exam is open book, but you can bring only non-electronic resources (e.g., books and notes, but for best results rely on your own notes).

Homeworks:

5 homeworks will be given throughout the class, see the Schedule here for the list of corresponding dates.

Collaboration is encouraged for the homeworks (and should be disclosed), but the final solutions have to be your own and copying of others’ work is forbidden.

Topics not covered

These are important topics we will not cover, but you might want to pursue in the future for fuller understanding of the field. Feel free to ask about them during office hours. In bold we have marked the topics on which someone from the instructor + TAs team could provide you with more information:

Analog quantum dynamics: Hamiltonians
Noisy quantum dynamics: Kraus operators, Lindblad Master Equation, Quantum Trajectories, CPTP maps
Efficient classical simulations of Stabilizer states and Clifford circuits
Noisy Quantum Computation: Fault tolerance, Fault tolerant syndrome measurement, Transversal gates, Magic states
Continuous variable quantum information, Bosonic codes, Gaussian quantum information
Supremacy experiments, sampling
Quantum chemistry simulations
Adiabatic quantum computation
Quantum optimization algorithms
Quantum machine learning
Cluster state computation, one-way quantum computers
Compilation of quantum circuits
Quantum non-locality and no-signalling correlations, quantifying entanglement
Quantum uncertainty principle
Quantum guessing games and non-local games
Quantum thermodynamics
Quantum resource theories
Quantum sensors
Quantum information in curved space-time, applications to Fundamental Physics, Cosmology, High Energy Physics, Black holes
Quantum Cryptography beyond high-level explanation of Quantum Key Distribution

Grading criteria

50% homeworks (counting only the n-1 best homeworks, discarding the grade from the worst one – consider this one “freebie” for when you need an extension)
20% midterm
30% final

Homework late return policy: each day the homework is late, the grade for that particular homework is lowered by a factor of 0.9, compounding.

Homework ratake: For one of the homeworks you can schedule an oral re-take within one-week of the grade release date.

Grading scale

Letter scale, listed below. Percentage grades will be rounded to the nearest integer. Adjustments might be made if rescaling/curving is needed.

%	Letter
93-100%	A
90-92%	A-
87-89%	B+
84-86%	B
80-83%	B-
77-79%	C+
74-76%	C
70-73%	C-
67-69%	D+
64-66%	D
below 63.5%	F

Attendance Policy

Regular course participation is crucial to success in this course as we will be covering large amount of material and the new sections will build on the previous ones. However, there is no formal attendance requirement.

Collaboration Policy

I support discussing the homeworks with fellow classmates to learn from each other. However, all of the content you submit needs to be produced independently, in your own words and based on your understanding of the solution. Copying of written homeworks is not permitted.

For the take-home midterm no communication with other students, neither in person nor on fora, is permitted.

Homework and take-home midterm late return policy

Each day the homework is late, the grade for that particular homework is lowered by a factor of 0.9, compounding. The late homeworks are not accepted beyond the 4th day after the deadline.

For the take-home midterm no credit will be given for late submissions.

Exam rescheduling policy

There are no scheduled make-up exams. Make-up exams and/or homework and take-home midterm extensions will only be offered to students with legitimate conflicts or unanticipated emergencies that can be documented in advance (when possible) or after the fact.

Course Communication and Management

The course materials and discussions will be hosted on Canvas, while Gradescope will be used for posting and uploading assignments and their solutions. With regard to Canvas discussions, we encourage you to help each other and try to answer other students’ questions if you feel you can contribute to the discussion. Of course you should not ask the homework question on the forum. The TA or the instructor will respond to your question if we feel that no reasonable answer has been provided within 24h. Please note that for a question posted on Friday, we do not guarantee reply until Monday and we also do not promise reply during holidays.

Use of Tools (solvers, textbooks, AI, etc) during exams and for homework

As long as you disclose the use, you can use any non-sentient tool you can think of to help with take-home exams and homework. That includes:

textbooks
search engines
numerical or symbolic software
AI language models and chat tools (e.g. ChatGPT)

However you have to disclose that you used such a tool. In particular, if you find a creative use of such a tool, you might be asked to demonstrate to the class the new technique you have discovered.

Beware, AI language models like ChatGPT might very often produce absolute garbage nonsense while presenting it with a veneer of authority and certainty.

For the final exam you are permitted only non-electronic tools. You can bring a hundred kilograms of books if you want, but you would probably obtain better results if you prepare your own summary notes.

You are not permitted to copy the work of another sentient being for any exam or homework.

Academic Honesty Statement

Copying of written homeworks, or exams or “teamwork” on an assignment (unless teaming is explicitly part of that assignment) is not permitted. You can talk to other students about the assignments, and ask/answer questions - it is great to learn from each other - but the work you hand in must be your own. A student found copying the work of others will receive a grade of F for the course. If you are having trouble with an assignment or if you are having trouble meeting a deadline, see the instructor or the TA; we will bend over backwards to help you but we will not tolerate cheating. Please read the UMass Academic Code of Conduct Policy.

Accommodation Statement

The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course. If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements. For further information, please visit Disability Services.

Title IX Statement

In accordance with Title IX of the Education Amendments of 1972 that prohibits gender-based discrimination in educational settings that receive federal funds, the University of Massachusetts Amherst is committed to providing a safe learning environment for all students, free from all forms of discrimination, including sexual assault, sexual harassment, domestic violence, dating violence, stalking, and retaliation. This includes interactions in person or online through digital platforms and social media. Title IX also protects against discrimination on the basis of pregnancy, childbirth, false pregnancy, miscarriage, abortion, or related conditions, including recovery. There are resources here on campus to support you. A summary of the available Title IX resources (confidential and non-confidential) can be found at www.umass.edu/titleix/resources. You do not need to make a formal report to access them. If you need immediate support, you are not alone. Free and confidential support is available 24 hours a day / 7 days a week / 365 days a year at the SASA Hotline 413-545-0800.

Accommodation Statement

Course Inclusiveness Statement

No matter who you are or how you define yourself you are welcome in this class. Each person here is a human being deserving of dignity and respect. My goal is to help you learn the subject matter in a way that you will find useful, and to help you have an enjoyable and empowering experience in doing so. It is important to keep in mind that we are all coming to this class with different backgrounds. We are all here to learn together! There are no stupid questions! From time to time, I may enlist some students to help others in class. If I ask you to help, remember that we all have different modes of learning, and there is no stigma to be associated with needing assistance. Please reach out to me if you have any concerns.

Pronouns Policy Statement

Everyone has the right to be addressed by the name and pronouns that they use for themselves. You can indicate your preferred/chosen first name and pronouns on SPIRE, which appear on class rosters. I will do my best to ensure that I address you with your chosen name and pronouns. Please let me know what name and pronouns I should use for you if they are not on the roster. Please remember: A student’s chosen name and pronouns are to be respected at all times in the classroom.

Extra-terrestial Policy Statement

Extra-terrestials sometimes try to kidnap students during lectures as has been evidenced here. To prevent these type of incidents, all students who notice any suspiciously behaving aliens should report that to the lecturer.