Introduction to Quantum Computing Winter 2026

This is the common course webpage for both CSE622 (4-credit) and CSE622A (2-credit) courses.

CSE622 (4 credit)

This is an introductory course on quantum computing. Quantum computing platforms and techniques leverage the principles of quantum mechanics to solve certain problems more efficiently compared to classical platforms. In this course a student will learn about the different ways a quantum computing platform can be used to design solutions from the perspective of computer science. The course will not require the knowledge of quantum mechanics; instead, it will introduce the postulates of quantum computing, the operations of a quantum computer, and the design principles of a quantum algorithm using linear algebra and probability. It will also cover a few quantum communication protocols like quantum teleportation, super-dense coding, a few well-known algorithms like Deutsch-Jozsa, Grover search, Shor’s factoring. The students will learn how to use different algorithmic toolkits like amplitude amplification, quantum Fourier transform, phase estimation, eigensolvers, hybrid quantum-classical approaches, Hamiltonian simulation, etc. The course will expose the students to modern concepts like NISQ, FTQC, error-correction, hybrid approaches, solvers for optimization problems, etc. The homeworks will involve implemeting solutions on quantum simulators and/or quantum backends. There will be a project or term paper that will involve reading research papers.

Pre-requisites

Course Outcomes

  1. Students are able to explain and apply the principles of quantum computing.
  2. Students are able to explain the well-known protocols (teleportation, super-dense coding, QKD) and algorithms (Deutsch, Deutsch-Jozsa, Shor’s factoring, Grover search, VQE, QAOA, QUBO solvers).
  3. Students are able to design and analyse quantum algorithms, circuits, and protocols for real-world problems.
  4. Students are able to implement solutions on simulators and/or quantum backends.
  5. Students are able to explain the concepts of NISQ, FTQC, hybrid algorithms.

Grading

A student must get 20% or above either in the midsem or the endsem to pass the course (irrespective of the total of all components).

CSE622A (2 credit)

This is an introductory course on quantum computing. Quantum computing platforms and techniques leverage the principles of quantum mechanics to solve certain problems more efficiently compared to classical platforms. In this course a student will learn about the different ways a quantum computing platform can be used to design solutions from the perspective of computer science. The course will not require the knowledge of quantum mechanics; instead, it will introduce the postulates of quantum computing, the operations of a quantum computer, and the design principles of a quantum algorithm using linear algebra and probability. It will also cover a few quantum communication protocols like quantum teleportation, super-dense coding, a few well-known algorithms like Deutsch-Jozsa, Grover search, Shor’s factoring. The students will learn how to use different algorithmic toolkits like amplitude amplification, quantum Fourier transform, phase estimation, eigensolvers, hybrid quantum-classical approaches, Hamiltonian simulation, etc. The homeworks will involve implemeting solutions on quantum simulators and/or quantum backends.

Pre-requisites

Course Outcomes

  1. Students are able to explain and apply the principles of quantum computing.
  2. Students are able to explain the well-known protocols (teleportation, super-dense coding, QKD) and algorithms (Deutsch, Deutsch-Jozsa, Shor’s factoring, Grover search, VQE, QAOA, QUBO solvers).
  3. Students are able to implement solutions on simulators and/or quantum backends.

Grading

The difference between the 2-credit and the 4-credit version is that the latter will go in-depth in the design and analysis of quantum algorithm and also cover a few advanced topics, like fault-tolerance. The latter is also one of the bucket-A courses for the Minor in Quantum Technologies. Late-dropping of CSE622A is not allowed.

The course relies heavily on several topics of mathematics like linear algebra, probability, and algorithms.
You are expected to be comfortable with the following prior concepts:

If you are not familiar or comfortable with these concepts, then you must do a self-study before joining the course (you are recommend to solve a few problems in those concepts from any textbook; mere reading of concepts may not provide sufficient depth); this book chapter explains the linear algebraic concepts suitable for quantum computation in one place.

Modules

  1. Principles of quantum computing
  2. Quantum circuits and basic algorithms
  3. Applications of oracle-based quantum algorithms
  4. Applications of quantum Fourier transform and quantum phase estimation
  5. Quantum algorithms for optimization
  6. (Next modules are only for CSE622)
  7. Amplitude Amplification
  8. Quantum Fourier transform and phase estimation
  9. Hamiltonian simulation
  10. Error correction

Course Personnel and Office Hours



Debajyoti Bera - dbera@ - Thursday 5-6pm (email me if you want meet at some other time)

Weekly Schedule

Homeworks

Collaboration may be allowed in the later homeworks, but not in the initial ones. Particularly, if it is not mentioned, you should not consult any other person or any other resource for solving the questions.
  1. Homework 1
  2. Homework 2
  3. Homework 3
  4. Homework 4
  5. Homework 5
  6. Homework 6

Worksheets

Practice worksheets will be regularly announced. Students are encouraged to solve them on their own and discuss their solutions with the TA or the instructor.
  1. Worksheet 1
  2. Worksheet 2
  3. Worksheet 3
  4. Worksheet 4

Proctored Assessments