Quantum Computing

Course Title: Quantum Computing

Course No: ENCT 328

Nature of the Course: Theory + Lab

Semester: 5

Full Marks: 40 + 60 + 25

Pass Marks: 16 + 24 + 10

Credit Hours: 3

Course Description

This course introduces the principles, mathematical foundations, and computational models of quantum computing. It emphasizes algorithmic insights and offers practical experience with quantum programming environments.

Course Objectives

The objective of this course is to introduce the principles, mathematical foundations, and computational models of quantum computing. It emphasizes on algorithmic insights, and offer practical experience with quantum programming environments. By the end of the course, students will be able to represent and manipulate quantum states and gates, design and simulate basic quantum circuits, implement fundamental quantum algorithms and their speedup mechanisms, and explore applications of quantum machine learning (QML) techniques.

Course Contents

1. Fundamentals of Quantum Concepts

6 hrs8 marks

1.1. Classical versus Quantum computation

1.2. Qubits, superposition, measurement

1.3. Postulates of quantum mechanics

1.4. Dirac notation and Bloch sphere

2. Quantum Gates and Circuits

6 hrs8 marks

2.1. Quantum gates (X, Y, Z, H, Phase, CNOT)

2.2. Unitary operations and reversibility

2.3. Multi-qubit systems and tensor products

2.4. Circuit design and visualization

3. Entanglement and Quantum Communication

5 hrs7 marks

3.1. Entangled states and Bell theorem

3.2. EPR paradox

3.3. Quantum teleportation and superdense coding

3.4. Quantum key distribution protocols: BB84, E91

4. Quantum Algorithms

10 hrs12 marks

4.1. Quantum parallelism

4.2. Deutsch and Deutsch–Jozsa algorithms

4.3. Bernstein–Vazirani

4.4. Grover's algorithm

4.5. Shor's algorithm

5. Quantum Hardware and NISQ Era

6 hrs8 marks

5.1. Physical realization (Ion trap, superconducting, photonic)

5.2. Noise, decoherence, and qubit metrics

5.3. NISQ model and limitations

5.4. Quantum circuit execution on IBM quantum simulators

6. Quantum Error and Correction

4 hrs6 marks

6.1. Bit-flip and phase-flip errors

6.2. Simple quantum error detection

6.3. Shor's 9-qubit code

6.4. Surface code overview

7. Quantum Annealing, Optimization Algorithms

4 hrs5 marks

7.1. Principles of quantum annealing

7.2. Relationship with adiabatic quantum computation

7.3. Optimization problem formulation (QUBO, Ising models)

7.4. Quantum annealer architecture

8. Quantum Machine Learning

4 hrs6 marks

8.1. Introduction to QML and its importance

8.2. Quantum data representation and feature encoding

8.3. Variational Quantum Circuits (VQC)

8.4. Simple quantum classifiers and QNN concepts

8.5. Hybrid quantum-classical learning models

Laboratory Works

Practical sessions covering IBM Qiskit/Quantum Composer labs, implementation of Grover's or teleportation circuit, Shor's algorithm, applications in combinatorial optimization, hands-on simulation using quantum annealers, and a mini-project demonstration. (15 hours)

1.IBM Qiskit / Quantum Composer labs
2.Implement Grover's or teleportation circuit
3.Implement Shor's algorithm
4.Applications in combinatorial optimization (E.g., scheduling, routing, network optimization)
5.Hands-on simulation using quantum annealers
6.Mini-project demonstration

Text Books

1.Nielsen, M. A., Chuang, I. L. (2002). Quantum computation and quantum information. Cambridge University Press.
2.Bernhardt, C. (2019). Quantum computing for everyone. MIT Press.

Reference Books

1.Schuld, M., Petruccione, F. (2021). Machine learning with quantum computers. Springer.
2.Sahni, V. (2007). Quantum computing. Tata McGraw-Hill Publishing Company.
3.Akama, S. (2014). Elements of quantum computing: History, theories and engineering applications. Springer International Publishing.
4.Wichert, A. (2014). Principles of quantum artificial intelligence. World Scientific Publishing Co.

Notes:

This syllabus follows the official BCT curriculum of Tribhuwan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative. https://ioe.tu.edu.np/pages/computer-engineering-curriculum-structure-2635

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