METHODS IN QUANTUM EXPERIMENT AND MODELLING - 2024/5

Module Overview

This module will introduce students to research techniques that are used in or relevant to quantum computing and quantum technology via extended projects. The four-week projects will cover a variety of relevant topics, many of which are explicitly quantum in nature, and also some that are classical problems that might be sped up with quantum computers. Students will gain technical experience using some combination of state-of-the-art equipment and software, analysing and working with large data sets and in problem solving.

This HE7 module provides an opportunity for students to experience an open-ended activity, in which the desired outcomes are not well defined in advance. This project-based work is typically less structured than for earlier levels. Students are expected to take more responsibility for the planning and direction of work than in undergraduate practical activities. The goal is to help prepare students for independent, research or creative development work as part of a team.

Module provider

Mathematics & Physics

Module Leader

DOHERTY Daniel (Maths & Phys)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

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

Module Availability

Semester 1

Prerequisites / Co-requisites

N/A

Module content

Indicative content includes:

• An introduction to the module and module structure.


• Students perform two projects during the semester.

Indicative list of projects may include:

• Quantum NOT gates in neutral atoms: use of radio wave pulses to produce a quantum gate in rubidium vapour.


• Quantum communication protocols: Construction of optical fibre communications system designed to simulate quantum key distribution.


• Quantum technology: Optical and electrical characterisation of quantum systems, involving an introduction to computer-controlled instrumentation with LabVIEW.


• Quantum computing: Introduction to quantum algorithms using classical simulators, practical use of Qiskit.


• Nuclear theory/ modelling: exploring nuclear models using existing codes; application to structure properties such as mass, size and shape, and reactions between nuclei .


• Soft matter theory/ modelling: Simulations of modern soft matter or biological physics.


• Classical Boolean logic circuits: design and implementation of electronic information processing circuits.

Assessment pattern

Alternative Assessment

For the Oral exams/presentations: In case of circumstances that prevent a complete team presentation on the time-tabled day, it may be necessary to perform the exam/presentation at a later date (possibly remotely e.g. by Teams). For reassessment of individual students during the LSA period, presentations will be shorter, individual presentations remotely e.g. by Teams.

Assessment Strategy

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

• 
  practical and computation skills, project planning and teamwork. 
  

Thus, the summative assessment for this module consists of:

• Report: one report on each of the two projects on the experimental/ modelling projects, presented in a research paper format (completed individually, not as a group), Learning Objectives 1-5. The first Report will be formatively assessed and only the second will be summative. In the report weight will be allocated for the open developments beyond the script instructions, with equal weight given to well reasoned failures and the learning that results as well as to successes in order to be an authentic assessment of report writing (LO4).


• Oral Presentation: One group oral presentation and interview on each of the two experimental/ modeling projects (equally weighted). The Oral presentations will be as a group, and should aim to convey understanding to other students that have not performed the same project, i.e. giving an authentic assessment of the experience of presenting scientific/technical reporting in research and industry LO1-5;


• Essay: An essay-style coursework where students will be asked (individually, not as a group) to describe the potential future impacts of quantum computing (or quantum technology more widely) on the experiments/simulations performed (LO6).

Feedback and formative assessment:

Feedback will be provided throughout the projects by experts in the relevant techniques and methodologies. Formative feedback will be provided by the assessment of the first project Report.

Module aims

• To enhance problem-solving skills, data analysis proficiency, and independent research capabilities by planning, implementing, and performing experimental and/ or modelling work.
• To develop presentational skills by explaining a complex, technical topic in a selective, succinct and easily comprehensible way.
• To prepare students for future team and project work including in future employment.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to

• combine student-led and flipped learning (in which students study the project topic before the practical session in order to maximise their contribution to the project and their use of staff support). This will enhance resourcefulness and employability.


• Encourage effective working in small groups to achieve a collective goal, by allocating tasks among the members including all aspects of planning, literature scholarship, technical activities, and presentation development/production. This task division will be monitored to ensure inclusion, avoidance of stereotyping, and development of cultural capabilities.

 

Thus the learning and teaching methods include

• An introduction to the module, including
  
  
  
  
  • report writing and presentation skills and
  
  
  • interview and project planning techniques.
  
  
  
  


• At the start of each project teaching staff will
  
  
  
  • lead the setup of equipment,
  
  
  • share relevant resources (e.g. a short lab script, instruction manuals, and relevant research papers) and
  
  
  • deliver an initial tutorial on the operation of equipment/ relevant techniques.
  
  
  
  


• Demonstrators will be available to assist with and troubleshoot on the project.

A list of projects is released to students in at the start of semester. Students will be allocated two projects to perform over semester, with an element of student choice depending on availability. Students work together in small groups on the project and would write an individual short research paper and then be interviewed on/present their results.

A trusted environment will be developed in which students are encouraged to make creative proposals for extensions with the open-ended nature of the projects. These extensions may or may not be successful, and students will be encouraged to be adventurous as possible, with high value given to reasoned failures with learnings that are well explained after the fact. Students can demonstrate resourcefulness, self-reliance and reliance on others in the team to develop understanding of the project and extensions.

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

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: PHYM067

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:

Digital Capabilities Throughout the module, deepening on the nature of the projects, students will engage with large and complex datasets (‘big data’) and/or modelling which will develop their computational skills in analysing this data using both Python and other bespoke computational languages. Assessments will give experience in graphical presentation and technical writing software.

Employability the module introduces learners to experimental equipment and modelling techniques used by professional scientists in both industry and academia. Students are given more responsibility for planning the project work (both experimental and theoretical), including the relevant health and safety and technical aspects and work together in small groups to produce a succinct report and a presentation summarising the work. The module, therefore, represents a key opportunity to practise and develop problem solving skills. Authentic assessments will give experience in conveying scientific/technical information in an appealing and accessible way.

Resourcefulness and Resilience Problem solving is a key component of this module with students given the opportunity to tackle more involved problems with more freedom and over a longer period of time. Students will be required draw upon individual and collective resourcefulness and develop a problem-solving mindset as they risk assess, adapt and respond to challenges faced over an extended experiment/modelling project.

Programmes this module appears in

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 2024/5 academic year.