Syllabus
General Electives
Unit I
Quantum Computation: History & Overview, Review of linear algebra: Dirac notation, Hilbert spaces, Unitary, Hermitian, and Normal matrices, Inner product, Outer product, Tensor product, Postulates of Quantum Mechanics, Stern and Gerlach experiment, Qubit, Bloch Sphere
Unit II
Circuit model of Quantum Computing: Quantum gates and Circuit, Qiskit programming. Entanglement: Bell state, Quantum Teleportation, Superdense coding, Phase kickback, No-cloning theorem, Quantum parallelism, Deutsch-Jozsa algorithm, Bernstein-Vazirani algorithm, Grover search algorithm
Unit III
Quantum Fourier Transform, Quantum Phase Estimation, Shor’s algorithm, Quantum Error Correction, Gottesman-Knill Theorem, Surface codes, Quantum Machine Learning : Data encoding – Basis encoding, Amplitude encoding, Hamiltonian Encoding, Swap test, Q-means clustering.
Objectives and Outcomes
Course Objectives
- The objective of this course is to provide a strong foundation in quantum computing theory and practical applications.
- It introduces the basic principles of quantum mechanics, qubits, circuit model of quantum computing etc and provides a hands-on experience on programming quantum computer using IBM Qiskit.
- It also includes an introduction to quantum machine learning.
Course Outcomes
CO1: Able to explain the fundamental concepts of quantum mechanics and quantum computing.
CO2: Able to represent quantum states and operations mathematically using Dirac notation and matrix representations.
CO3: Able to design and implement quantum circuits using Qiskit.
CO4: Able to develop simple quantum machine learning solutions.
CO-PO Mapping
| PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PSO1 |
PSO2 |
| CO |
| CO1 |
3 |
2 |
2 |
2 |
1 |
|
|
|
|
2 |
|
1 |
3 |
2 |
| CO2 |
3 |
3 |
3 |
2 |
3 |
|
|
|
1 |
2 |
|
2 |
3 |
2 |
| CO 3 |
3 |
3 |
3 |
3 |
3 |
|
|
|
|
2 |
|
2 |
3 |
2 |
| CO4 |
3 |
3 |
3 |
3 |
3 |
1 |
|
|
|
2 |
|
2 |
3 |
2 |
Evaluation Pattern
Evaluation Pattern: 70:30
| Assessment |
Internal |
End Semester |
| MidTerm Exam |
20 |
|
| Continuous Assessment – Theory (*CAT) |
10 |
|
| Continuous Assessment – Lab (*CAL) |
40 |
|
| **End Semester |
|
30 (50 Marks; 2 hours exam) |
*CAT – Can be Quizzes, Assignments, and Reports
*CAL – Can be Lab Assessments, Project, and Report
**End Semester can be theory examination/ lab-based examination/ project presentation
Text Books / References
Textbook(s)
David McMahon, “Quantum Computing Explained”, Wiley-IEEE Computer Society Press, 2007.
Maria Schuld, Francesco Petruccione, “Machine Learning with Quantum Computers”, Springer International Publications, 2021.
Venkateswaran Kasirajan, “Fundamentals of Quantum Computing -Theory and Practice”, Springer, 2021.
Reference(s)
Nielsen MA, Chuang IL. “Quantum computation and quantum information”.Cambridge university press;2010 Dec 9.
Eleanor Rieffel and Wolfgang Polak, “Quantum Computing: A Gentle Introduction”,2011 Edition, MIT Press.
Chris Bernhardt, “Quantum Computing for Everyone (The MIT Press)”,2019.
