QUANTUM TECHNOLOGY - 2024/5

Module Overview

This module comprises two independent halves, on quantum simulations (Q Sim), and quantum biology (Q Bio).

• Quantum Simulation. The quantum simulation part of this module introduces students to the use of quantum computers in the simulation of physical systems using mapping of Hamiltonians from standard quantum mechanics to a representation suitable for application on quantum computers, along with a study of wavefunction ansatz design, algorithms, and error mitigation and correction.
• Quantum biology is the study of how quantum mechanical phenomena, such as quantum superposition, tunnelling, and entanglement, can be exploited by living systems to provide evolutionary and/or biological advantages. This half module will cover a range of biological molecules and processes that may exploit quantum effects, such as magnetoreception, photosynthetic light harvesting, and DNA mutations. The goal of this half module is to develop an understanding of how quantum processes could have an impact in nature and how this knowledge can be further used for applications in health and medical sciences. In addition, using research progress in the field of quantum biology and illustrative examples, this course will help to develop an experimental and theoretical understanding of how quantum processes may play a crucial role in maintaining the non-equilibrium state of the biomolecular systems. Many of the subjects acquired through this half module are likely to be of potential use in future project work,

Module provider

Mathematics & Physics

Module Leader

STEVENSON Paul (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 2

Prerequisites / Co-requisites

None

Module content

Indicative content for the Quantum Simulation part:

• Quantum systems for simulation. Suitable example quantum many-body systems are discussed, drawing from examples in chemistry (e.g. molecular systems), condensed matter (e.g. spin systems), and nuclear physics. Representation of the Hamiltonian is described in second quantization notation.
• Hamiltonian Encoding. Methods of encoding Hamiltonians on quantum computers are presented, mapping from the second quantized notation to qubit spins via the Jordan-Wigner and other mapping methods, discussing and exploring the relative merits of different methods, dependent on the problem at hand and the quantum hardware available.
• State encoding. Methods of preparing entangled ansatz states representing many-body quantum wave functions for use in quantum simulation or optimization algorithms.
• Simulation algorithms. Methods of extracting physical information from the combination of wave function and Hamiltonian: Time-evolution and Trotterization; variational methods including the Variational Quantum Eigensolver.
• Error Mitigation. Sources of error in quantum simulation and methods for assessing and reducing error on current quantum hardware. o Latest research. The latest research will be used to update content to take account of hardware capabilities and algorithms.

• The history of quantum biology. We begin with a historical overview, tracing the origins and development of Quantum Biology including some basic classical biology.
• Spin-dependent chemical reactions in biology. The radical pair mechanism is an example of the interaction between electron spins in chemical reactions, and governs how some birds sense magnetic fields. We will examine how spin plays a role in diverse biological processes, and are integral to enzymatic catalysis and signal transduction. Chirality-induced spin selectivity effects.
• Quantum processes. Quantum processes in photosynthetic light-harvesting systems showcase the remarkable efficiency of quantum phenomena in energy transfer. Coherent quantum interactions can take place in biomolecules, such as chromophore-proteins. Quantum tunnelling of protons is important in DNA mutations.
• Quantum technology for biology. Quantum technology can provide enhanced sensing capabilities, and quantum computing can help understand biomolecular reactions, such as in the nitrogenase molecule, which, if we could mimic it industrially would lead to enormous savings in energy and carbon emissions from the agri-chemicals industry.
• Latest research. The latest research will be used to inform content in both the applications and modelling.

Assessment pattern

Alternative Assessment

In situations where the Q Comms Project (Group part) requires an alternative or reassessment it will be replaced with an individual literature review. In cases where an alternative re-assessment to an in-class group presentation is required, the alternative assignment will be an individual report/dissertation (Q Bio).

Assessment Strategy

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

• adaptability, collaboration skills resulting in team contributions to group outcomes (Q Bio).
• both individual knowledge and problem-solving abilities

• The Quantum Simulation project assessed by oral means, in which students will demonstrate synthesis and application of the module content (Q Sim).
• The Quantum Biology project will take the form of a group assignment, to be presented to class members during the last lecture session plus individual deliverables submitted following the group presentation. The project will evaluate recent research in quantum biology. Students will be assigned a group and choose scientific literature to discuss the limitations of current methods, together with research developments that aim to improve this in the areas of quantum biology. 

• On-line class quizzes will precede the oral assessment to give students formative feedback on progress (Q Sim).
• Formative Feedback and Coaching will be provided that is timely and constructive throughout the project duration to guide project groups in their implementation. (Q Bio)
• A team project Proposal and Plan, will be submitted early in the QBio project for formative assessment, outlining the objectives for each individual (i.e. with different tasks for each member of the team), a scope, methodology, and timeline. [Assessed on the clarity, feasibility, and appropriateness of the methodology, techniques and tools.] (QBio)
• SurreyLearn quizzes (Q Bio)

Module aims

• To give students a comprehensive introduction to the ideas of quantum simulation (Q Sim)
• To ensure students can use the second quantization notation of quantum mechanics, and translate Hamiltonians to Pauli form for implementation on quantum computers (Q Sim)
• To embed strategies and techniques for making suitable wave function ansatzes (Q Sim) strategies
• To impart a range of standard algorithms and ensure students can use them in unseen cases (Q Sim)
• Provide a solid foundation of basic quantum biology knowledge that will facilitate the students' understanding of the biological molecules and biomolecular systems that give rise to the quantum effects (Q Bio).
• Develop critical thinking skills to enhance confidence in students' ability to undertake practical work in their dissertation projects (Q Bio).

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

• Give students the skills to take a physical problem and map it onto the formalism of quantum computation (Q Sim).
• Enhance students' knowledge of quantum processes in nature (Q Bio).
• Develop the critical understanding of quantum biology concepts (Q Bio).
• Improve students' group collaboration and presentation skills (Q Bio). 

• A combination of traditional lectures backed up with guided study to stimulate uptake of subject knowledge (Q Sim and Q Bio)
• hands-on sessions in a computer laboratory to work through examples of quantum simulation. During some of the computer lab sessions, the students will have an opportunity to have supervised time working on the assessment associated with this part of the module (Q Sim).
• tutorial demonstration of solutions to key problems, with practice for students both before and after having attempted them for formative feedback (Q Bio)
• tutorials to discuss group presentation assessments and class discussion about limitations and challenges in quantum technology and quantum biology. (Q Bio)

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: PHYM073

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:

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. This will be discussed in terms of quantum simulation scaling with problem size much better than classical algorithms, hence that the hardware and resource implications for quantum simulation are, in principle, much less than with classical computing resources (Q Sim). The Quantum Biology half module encompasses quantum processes in biology, such as those that may be involved in photosynthesis and nitrogen fixation in plants, which can have immense importance for sustainability. (Q Bio)

Digital Capabilities: In this module we study a revolution in digital capabilities: the quantum computer, and the corresponding advantages for communications (Q Comms). The module covers advanced (quantum) computational/simulation methods, which are wholly a subset of digital capabilities (Q Sim).

Resourcefulness and Resilience: By participating in group activities, students have the opportunity to demonstrate resourcefulness and resilience through problem-solving, time management, collaboration, conflict resolution, adaptability, seeking feedback, and reflective practices. These skills are invaluable in navigating challenges, both within group settings and in real-world situations, fostering their personal growth and success (Q Bio). Quantum biology is a very interdisciplinary field, and the group presentations will require students to read, absorb and speak to articles and papers in physics, chemistry and biology (Q Bio).

Employability. The group project will enhance students' employability by providing them with practical experience in collaboration, communication, problem-solving, adaptability, accountability to others, time management, leadership, professionalism, and networking. These skills are transferable to various professional settings and contribute to their readiness for the workforce. (Q Bio).

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.