CS41915 Quantum Computing
Introductory description
Quantum computing is an interdisciplinary field that lies at the intersection of computer science, mathematics, and physics. This computational paradigm relies on principles of quantum mechanics, such as superposition and entanglement, to obtain powerful algorithms.
Module aims
This module aims to provide a selfcontained, comprehensive introduction to quantum computing, focusing on the design and analysis of quantum algorithms, as well as covering topics in quantum information and quantum cryptography, such as: quantum teleportation, quantum money, and postquantum cryptography.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Quantum computing — motivation, foundations, and prominent applications.
Review of linear algebra in the context of quantum information, Dirac’s bracket notation, limitation of classical algorithms.
The four postulates of quantum mechanics, qubits, quantum gates and circuits.
Basic quantum algorithms I — Deutsch’s algorithm, analysing quantum algorithms, and implementing quantum circuits via QISKIT.
Basic quantum algorithms II — Simon’s problem and the Bernstein Vazirani algorithm.
Grover’s quantum search algorithm, the BBBV Theorem, and applications of Grover’s algorithm.
RSA, and Shor’s integer factorisation algorithm.
Introduction to quantum cryptography (postquantum security, quantum key distribution).
Introduction to quantum information (superdense coding, nocloning theorem, quantum teleportation) Applications (quantum money, the ElitzurVaidman bomb).
Learning outcomes
By the end of the module, students should be able to:
 Understand the implications of quantum computing on cryptography and security: Understand the foundations of postquantum cryptography. Hack the RSA cryptosystem via a quantum computer. Use quantum mechanics to obtain a monetary scheme.
 Understand the quantum computing paradigm: Have an overview of a range of project management techniques Understand how failure to correctly manage a project can lead to failure. Understand how project management techniques provide quantifiable metrics for project progress
 Understand the power and limitation of quantum computers: Understand the underlying power of quantum mechanics for computation. Identify problems for which a quantum speedup is possible. Understand the fundamental limitations of quantum algorithms.
 State the four postulates of quantum mechanics and their application to computation: Design and analyse quantum algorithms. Grasp the notions of quantum states, unitary evolution, measurements, andcomposite systems. Restate the postulates in terms of density matrices.
 Understand the principles of quantum information andquantum communication: Understand quantum teleportation and its limits. Describe the framework of quantum errorcorrecting codes. Discuss Everett’s many worlds interpretation.
 Analyse fundamental quantum algorithms: Shor’s algorithm. Grover’s search. The BersteinVazirani algorithm. Simon’s problem. The DeutschJozsa paradigm.
Indicative reading list
Please see Talis Aspire link for most up to date list.
View reading list on Talis Aspire
Subject specific skills
Designing and analysing quantum algorithms.
Transferable skills
Understanding quantum mechanics and the power of quantum computing.
Study time
Type  Required 

Lectures  30 sessions of 1 hour (20%) 
Seminars  10 sessions of 1 hour (7%) 
Private study  110 hours (73%) 
Total  150 hours 
Private study description
Revising linear algebra, the postulates of quantum mechanics, the principles of superposition, measurement, and entanglement. Analysing the algorithm discussed in class, including: Deutsch’s algorithm, the DeutschJosza algorithm, the BersteinVazirani algorithm, Grover’s algorithm, Simon’s algorithm, and Shor’s algorithm.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group D5
Weighting  Study time  

Problem Set 1  10%  
Problem Set 

Problem Set  10%  
Problem Set 2 

Problem Set  10%  
Problem Set 3 

Inperson Examination  70%  
CS419 examination.

Assessment group R1
Weighting  Study time  

Inperson Examination  Resit  100%  
CS419 MEng resit Examination

Feedback on assessment
Comments on assignments alongside a mark will be provided, solutions will be discussed in the seminars.
Prerequisites
Student must have studied the material in:
CS130 + CS131: Mathematics for Computer Scientists 1 + 2, or
CS136 + CS137 Discrete Mathematics and its Applications 1 + 2, or
MA106 Linear Algebra + ST111 Probability A
Courses
This module is Optional for:
 Year 5 of UCSAG504 MEng Computer Science (with intercalated year)

UCSAG503 Undergraduate Computer Science MEng
 Year 4 of G503 Computer Science MEng
 Year 4 of G503 Computer Science MEng
This module is Option list A for:
 Year 4 of UCSAG4G3 Undergraduate Discrete Mathematics
This module is Option list B for:
 Year 4 of UCSAG408 Undergraduate Computer Systems Engineering