QUANTUM MACHINE LEARNING - 2027/8
Module code: PHYM082
Module Overview
Quantum machine learning (QML) is an emerging field at the intersection of quantum computing and data science, exploring how quantum systems can enhance learning, optimisation, and data analysis tasks. At the same time, quantum simulation remains one of the most promising near-term applications of quantum computers, enabling the study of complex quantum systems that are intractable for classical computation.
This module provides an integrated introduction to both quantum machine learning and quantum simulation. It develops an understanding of how quantum algorithms and variational methods can be applied to learning and optimisation problems, alongside the theoretical and practical frameworks required to simulate physical systems on quantum hardware. Emphasis is placed on mapping real-world problems to quantum representations, understanding algorithmic design, and evaluating performance under realistic hardware constraints.
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
Overall student workload
Independent Learning Hours: 60
Lecture Hours: 20
Laboratory Hours: 10
Guided Learning: 40
Captured Content: 20
Module Availability
Semester 2
Prerequisites / Co-requisites
N/A
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.
¿Latest research. The latest research will be used to update content to take account of hardware capabilities and algorithms.
Indicative content for Quantum Machine Learning:
¿This part introduces the principles and techniques of machine learning in a quantum context, building on prior knowledge of data science and quantum algorithms.
¿Overview of classical machine learning concepts relevant to quantum approaches
Variational quantum circuits for supervised and unsupervised learning tasks
¿Quantum kernel methods and feature spaces
Quantum reservoir computing
Applications of quantum computing to optimisation and learning problems
¿Relationship between quantum algorithms, optimisation, and training of quantum models
¿Limitations of current approaches and prospects for quantum advantage
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Oral exam or presentation | Quantum Simulation oral presentations / viva | 50 |
| Oral exam or presentation | Quantum Machine Learning oral presentations / viva | 50 |
Alternative Assessment
None
Assessment Strategy
The assessment strategy is designed to allow students to demonstrate:
- Understanding of advanced quantum algorithms for simulation and learning
- Ability to map physical and data-driven problems onto quantum computational frameworks
- Critical evaluation of algorithmic performance and hardware limitations
- Ability to communicate complex technical ideas clearly and effectively
The Quantum Simulation oral presentations / viva is designed to assess Learning Outcomes QSim1-QSim4 and the Quantum Machine Learning oral presentations / viva is designed to assess Learning Outcomes QML1-QML3.
Module aims
- To develop an understanding of how quantum computing can be applied to machine learning, optimisation, and data-driven tasks.
- To introduce the theoretical and practical frameworks for simulating quantum systems on quantum computers.
- To enable students to critically evaluate quantum algorithms and assess their performance and limitations on real hardware.
Learning outcomes
| Attributes Developed | Ref | ||
|---|---|---|---|
| 001 | To understand, to be able to explain, and to use, the second quantization formalism in quantum mechanics. | KC | QSIM1 |
| 002 | To be able to map general Hamiltonians into qubit / Pauli matrix form | KC | QSIM2 |
| 003 | To be able to make suitable wave function ansatzes, with physical insight from a problem at hand. | KC | QSIM3 |
| 004 | To understand quantum simulation algorithms and to be able to implement them with an understanding of errors and actual quantum advantage associated with real quantum computers. | KCPT | QSIM4 |
| 005 | Explain and critically evaluate key quantum machine learning approaches, including variational quantum circuits, quantum kernel methods, and reservoir computing. | KC | QML1 |
| 006 | Implement and analyse quantum machine learning models for classification or optimisation tasks, assessing their performance relative to classical methods. | KP | QML2 |
| 007 | Assess the potential for quantum advantage in machine learning applications, taking into account data encoding, noise, and hardware constraints. | KPT | QML3 |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
Learning and teaching methods include:
Lectures covering theoretical and algorithmic concepts
¿Computer-based workshops exploring quantum algorithms and simulation techniques
¿Tutorials focused on problem-solving and conceptual understanding
¿Guided study and discussion of current research literature
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: PHYM082
Other information
This module develops advanced knowledge and skills in quantum computing, with particular emphasis on applications in AI, Machine learning, simulation, optimisation, and machine learning. It supports employability in emerging quantum technology sectors, including quantum software, algorithm development, and data-driven quantum applications.
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 2027/8 academic year.