Module code: PHY3071

Module Overview

This module comprises two independent halves, on How to Make a Qubit (Qubits), and on Quantum Optimization (Q Optim).

  • Quantum technologies, including quantum computing, rely on the quantum mechanical principles of superposition and entanglement. Furthermore, this superposition and entanglement needs to be controlled in useful ways. In this module you will learn about what physical systems allow quantum technology production, and their limitations. Quantum computers are only one type of device that uses these principles, and several other technologies are being created that are also enhanced by use of superpositions. Others include atomic clocks and Magnetic Resonance Imaging, MRI. We will also learn about the errors that inevitably build up in quantum computers when quantum superpositions are disturbed, and the strategies that might be built in to correct them.

  • Quantum optimization is expected to take over a large number of demanding computational tasks, because it is capable of performing computations faster than their classical counterparts. Among many potential applications in logistics, aerospace, traffic control and in finance, which includes include pricing, risk management, and portfolio optimizations in financial markets, and indeed some banks have been investing in developing quantum computer algorithms.

Module provider

Mathematics & Physics

Module Leader

GINOSSAR Eran (Maths & Phys)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 69

Lecture Hours: 25

Tutorial Hours: 6

Guided Learning: 25

Captured Content: 25

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

Indicative content for the How to Make a Qubit part:

  • Qubit platforms. The DiVicenzo criteria specify the ingredients needed to build a quantum computer. We will perform a comparative study the characteristics of a variety of quantum technology platforms that provide the requisites (likely to include, but not limited to: Harmonic oscillators; Ion Traps; Photons; NMR; diamond NV; semiconductor spins).

  • Error correction. Not only do we need strategies to reduce errors in quantum computers for qubits with low fidelity, strategies for correction are essential, and this has important implications for the number of qubits required in a practical computer.

  • Other quantum technologies. We will also investigate the possibilities for other applications of quantum technology such as: Atomic Clocks; Magnetic Resonance Imaging; Sensors) [NB some specific hardware systems and applications are not included here because they will be explored in detail in other modules].

  • Latest research. The list of platforms covered, the details of their comparison, and the applications in quantum technology will be kept up-to-date with the latest research in the field.

Indicative content for the Quantum Optimization part:

  • Introduction to Quantum computing. This half-module will begin with a brief introduction to quantum computing with quantum circuits as well as discussing quantum annealing, both being relevant strategies for quantum optimization.

  • The Quantum Approximate Optimization Algorithm (QAOA)

  • Quantum annealers (QA)

  • Case studies of quantum optimization. The application of QAOA and QA and to applications and potential use cases such as in finance, logistics, communications, computer vision.

  • Latest research. The latest research will be used to update content to take account of hardware capabilities and algorithms

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Qubits open-book online assignment 10
Coursework Qubits coursework 20
Coursework Quantum Optimization Coursework 50
Examination Qubits Exam (1hr) 20

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:

  • individual knowledge, skill, and problem-solving abilities (Qubits, Q Optim);

  • The ability to use quantum optimizers for given simple optimization problems (Q Optim);

  • The ability to analyse the suitability of a given optimization problem for quantum optimization and devise a strategy to choose the correct algorithm (Q Optim)

Thus the summative assessment for this module consists of:

  • The Qubits on-line assignment, in which students answer short questions the module content covering LO 1-3 (Qubits)

  • The Qubits coursework in which students will demonstrate synthesis and application of the module content covering LO 1-3 (Qubits).

  • The Quantum Optimization project, covering LO4-6 (Q Optim).

  • The Qubits exam covering LO 1-3 (Qubits).

Formative assessment:

  • On-line class quizzes will precede the summative on-line assignment and exam to give students formative feedback on progress (Qubits).

  • There is feedback from tutorial assignments (Q Optim).


  • Verbal feedback is provided by the lecturer during the tutorials e.g., when exercises are worked out (Qubits Q Optim)

  • One-to-one advice in open office hours (Qubits and Q Optim)

Module aims

  • The module aims to give an understanding of the variety of modern quantum computer hardware, with comparative analysis of the advantages, disadvantages, and likely applications of each (Qubits)
  • Applications of quantum technology other than computers will also be explored, such as quantum sensing (Qubits)
  • From a hardware perspective, it is very important to understand how imperfections in quantum technology affect the ability to deliver large scale computers, and this module will cover hardware error correction strategies (Qubits)
  • To equip students with the understanding of the key difference between a classical and a quantum computers. (Q Optim)
  • To equip students with the understanding of the types of optimization challenges in industry and finance which can be handled by quantum optimization algorithms. (Q Optim)
  • To equip students with the basic understanding of the different approaches and quantum algorithms and how real-life problems could be translated and solved by quantum processors and quantum annealers. (Q Optim)

Learning outcomes

Attributes Developed
001 Compare and contrast the advantages and disadvantages of different quantum hardware systems (Qubits) CK
002 Calculate the strength and sequence of perturbation pulses needed to produce specific operations with a variety of quantum technology platforms (Qubits) CK
003 Analyse and present specific advances made in recent scientific literature results relative to the state-of-the-art in the relevant topic (Qubits) PT
004 To understand quantum simulation and optimization algorithms and to be able to implement them with an understanding of errors and actual quantum advantage associated with real quantum computers (Q Optim) CPT
005 Demonstrate an understanding of the principles of quantum circuits and quantum annealing. (Q Optim) CKT
006 Demonstrate working knowledge of applying quantum computing to optimization problems. (Q Optim) CKP

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

  • Expose students to the latest research in quantum technology hardware, including not only quantum computer hardware but also other technologies like quantum clocks, sensors etc. (Qubits)

  • Encourage critical thinking about hardware research results, in particular comparative assessment of benefits and barriers for any given technology (Qubits).

  • Give understanding of the way that quantum computer gates are translated into signals to hardware in the various implementations (Qubits)

  • Knowledge of strategies to deal with errors, in the present era of Noisy Intermediate Scale Quantum computing (Qubits)

  • Introduce basics of quantum circuits and quantum annealing (Q Optim).

  • Introducing the structure and flow of quantum algorithms for optimization and how they achieve the desired speedup compared to classical algorithms (Q Optim).

  • Give the students skills to translate a real-world optimization problem into a form that is suitable for processing on quantum processors (Q Optim)

  • Introduce students to the challenges and limitations of current hardware and how to decide on suitability of using quantum optimization (Q Optim)

Thus the learning and teaching methods include:

  • Traditional lecture-based sessions to cover background theory will be highly interactive and attendance is required, (Qubits and Q Optim)

  • Tutorials to gain practice in exam style questions aim to help students identify areas of weakness in a supportive environment (Qubits)

  • Typeset notes, containing exercises and examples, will be provided (Q Optim).

  • Computer laboratory-based tutorials (Q Optim)

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: PHY3071

Other information

The School of Mathematics and Physics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:

Sustainability. Optimization of resources is a central topic of discussion in quantum optimization, and this has a wider set of applications in resource management, crucial for sustainability (Q Optim).

Digital capabilities. In this module we study the hardware components of a revolution in digital capabilities: the quantum computer. (Qubits)

Resourcefulness and Resilience: In development of the simulation project/presentation students will develop ability to solve an extended challenge, and the necessary self-reliance (Q Sim). The knowledge on how to optimize (as applied here to various logistics, traffic, communications, finance and science) will be beneficial in a range of applications, thus enhancing the resourcefulness of the students. (Q Optim)

Global and Cultural capabilities: The module will provide ideas of quantum computers and how they compare to their classical counterparts, along with the areas in which they may transform the world. These ideas, in turn, are beneficial towards enhancing global and cultural intelligence of the students. (Q Optim)

Employability: This module teaches the structures of financial markets, along with the concepts of portfolio management, valuation, and risk management. All of these will significantly enhance the employability of the students in financial and related industries. (Q Optim) The market for graduates in quantum computing is expected to rise significantly in the future as the technology becomes more established, and background knowledge of the variety of platforms with their various advantages and disadvantages will be very beneficial (Qubits)

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Applied Quantum Computing MSc 1 Compulsory A weighted aggregate mark of 40% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.