MSc — 2027 entry Applied Quantum Computing

Quantum computers are built around a central processing unit which is a physical device operating according to the rules of quantum mechanics. This module teaches the principles of how quantum processors are built and how they function. The module follows the superconducting architecture, which currently has a significant role in the industry and has been widely studied and implemented. The module will start with reviewing the basic physics required to understand superconducting qubits and the theory of superconducting circuits. It will then continue to the operation of such qubits to perform gates, storage, and readout. After studying the basic building blocks and operations, the module will discuss early demonstrations of important algorithms on superconducting processors. These will lead us naturally to the important topics of improving performance through protection of coherence, advanced control, and validation. The last part of the module will focus on state-of-the-art superconducting processors, focusing on the various challenges in scaling up and the strategies that the industry is pursuing to overcome them.

Quantum computing exploits the principles of superposition and entanglement to perform certain computational tasks more efficiently than is possible using classical computers. While quantum computing relies on quantum physics, it is possible to understand and apply quantum computation without detailed prior training in quantum mechanics. This module provides an introduction to quantum computing from a computational and algorithmic perspective. It introduces the mathematical framework used to describe qubits and quantum logic, develops the concept of quantum circuits, and explores how quantum algorithms may be applied to optimization problems of practical relevance. The module emphasises how quantum information is processed, rather than how quantum hardware is physically realised. This module is suitable for students from Physics, Computer Science, Electronic Engineering, and related disciplines.

Quantum technologies exploit quantum mechanical phenomena such as superposition and entanglement to enable new classes of devices, including quantum computers, sensors, clocks, and imaging systems. Understanding these technologies requires an appreciation of both the underlying quantum physics and the practical constraints of real physical systems. This module provides an introduction to quantum technology from a physical and hardware-oriented perspective. It introduces the essential concepts of quantum physics needed to understand quantum devices, and explores how different physical systems can be engineered to act as qubits. Emphasis is placed on comparing quantum technology platforms, understanding their limitations, and assessing their suitability for different applications.

This module covers circuit-based quantum computers and some of the algorithms that can be implemented using them.  The idea of a classical computational algorithm and its complexity is introduced, and used as a language to discuss quantum computational algorithms.  Components of quantum circuits are discussed individually, and then built up to show how algorithms such as the quantum Fourier transform and Grover¿s search algorithm can be implemented, and how such algorithms can be used in applications such as factorizing integers using Shor¿s algorithm.  A suitable computational frameworkis introduced and used as a tool to implement the algorithms on simulated quantum computers.

All students aiming for the MSc degree qualification undertake an MSc dissertation project. Students choose a project either from a list of proposed topics within the University, or in some cases arrangement is made for the project to be undertaken in industrial, research or hospital-based environment. The majority of part-time students arrange to undertake the project in their place of work. Students are assigned a supervisor relating to the project chosen. Students undertaking their project outside of the University are assigned both an internal and an external supervisor. The work is assessed as follows:   Project write-up A write up of no more than 40 pages in total, including title page, brief abstract, text, diagrams and references must be submitted. Supervisors will give guidance on the layout of the project and the first draft of material where appropriate. When referencing in your written work, you should use the Harvard referencing system unless otherwise directed by the Module Co-ordinator. Further information relating to referencing in your work, can be found on the University Library website at http://www.surrey.ac.uk/library/subject/bibref/  

This module introduces programming in Python for data science, with a focus on data pre-processing, data mining and analysis, machine learning and deep learning. Besides the practical hands-on experience with writing code, this course also covers the theoretical background on different data analysis techniques and machine learning approaches. The goal is to develop an understanding of how information can be extracted from data and how this information can be further used to make predictions, but importantly how this is done practically in terms of writing clear and transparent source code. Using real-world data sets and illustrative examples, this course will help to develop a theoretical understanding of data science as well as practical experience by developing useful software tools. Many of the techniques acquired through this module are likely to be of potential use in the dissertation project.

Quantum Mechanics topics are essential building blocks for our understanding of many physical systems. The module assumes basic knowledge in quantum mechanics from modules in earlier years, but will provide a review at the beginning. Topics include a review of quantum mechanics, operator methods and applications to the harmonic oscillator, spin & angular momentum, symmetries in quantum mechanics. The second half of the module then moves beyond isolated quantum systems by addressing the topic of Quantum Entanglement and Quantum Coherence. Here the important concepts of entanglement and decoherence will be studied by developing an understanding of open quantum systems and the density matrix formalism.

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.

Quantum technologies exploit quantum mechanical phenomena such as superposition and entanglement to enable new classes of devices, including quantum computers, sensors, clocks, and imaging systems. Understanding these technologies requires an appreciation of both the underlying quantum physics and the practical constraints of real physical systems. This module provides an introduction to quantum technology from a physical and hardware-oriented perspective. It introduces the essential concepts of quantum physics needed to understand quantum devices, and explores how different physical systems can be engineered to act as qubits. Emphasis is placed on comparing quantum technology platforms, understanding their limitations, and assessing their suitability for different applications.

Quantum computing exploits the principles of superposition and entanglement to perform certain computational tasks more efficiently than is possible using classical computers. While quantum computing relies on quantum physics, it is possible to understand and apply quantum computation without detailed prior training in quantum mechanics. This module provides an introduction to quantum computing from a computational and algorithmic perspective. It introduces the mathematical framework used to describe qubits and quantum logic, develops the concept of quantum circuits, and explores how quantum algorithms may be applied to optimization problems of practical relevance. The module emphasises how quantum information is processed, rather than how quantum hardware is physically realised. This module is suitable for students from Physics, Computer Science, Electronic Engineering, and related disciplines.

This module introduces programming in Python for data science, with a focus on data pre-processing, data mining and analysis, machine learning and deep learning. Besides the practical hands-on experience with writing code, this course also covers the theoretical background on different data analysis techniques and machine learning approaches. The goal is to develop an understanding of how information can be extracted from data and how this information can be further used to make predictions, but importantly how this is done practically in terms of writing clear and transparent source code. Using real-world data sets and illustrative examples, this course will help to develop a theoretical understanding of data science as well as practical experience by developing useful software tools. Many of the techniques acquired through this module are likely to be of potential use in the dissertation project.

This module covers circuit-based quantum computers and some of the algorithms that can be implemented using them.  The idea of a classical computational algorithm and its complexity is introduced, and used as a language to discuss quantum computational algorithms.  Components of quantum circuits are discussed individually, and then built up to show how algorithms such as the quantum Fourier transform and Grover¿s search algorithm can be implemented, and how such algorithms can be used in applications such as factorizing integers using Shor¿s algorithm.  A suitable computational frameworkis introduced and used as a tool to implement the algorithms on simulated quantum computers.

Quantum computers are built around a central processing unit which is a physical device operating according to the rules of quantum mechanics. This module teaches the principles of how quantum processors are built and how they function. The module follows the superconducting architecture, which currently has a significant role in the industry and has been widely studied and implemented. The module will start with reviewing the basic physics required to understand superconducting qubits and the theory of superconducting circuits. It will then continue to the operation of such qubits to perform gates, storage, and readout. After studying the basic building blocks and operations, the module will discuss early demonstrations of important algorithms on superconducting processors. These will lead us naturally to the important topics of improving performance through protection of coherence, advanced control, and validation. The last part of the module will focus on state-of-the-art superconducting processors, focusing on the various challenges in scaling up and the strategies that the industry is pursuing to overcome them.

All students aiming for the MSc degree qualification undertake an MSc dissertation project. Students choose a project either from a list of proposed topics within the University, or in some cases arrangement is made for the project to be undertaken in industrial, research or hospital-based environment. The majority of part-time students arrange to undertake the project in their place of work. Students are assigned a supervisor relating to the project chosen. Students undertaking their project outside of the University are assigned both an internal and an external supervisor. The work is assessed as follows:   Project write-up A write up of no more than 40 pages in total, including title page, brief abstract, text, diagrams and references must be submitted. Supervisors will give guidance on the layout of the project and the first draft of material where appropriate. When referencing in your written work, you should use the Harvard referencing system unless otherwise directed by the Module Co-ordinator. Further information relating to referencing in your work, can be found on the University Library website at http://www.surrey.ac.uk/library/subject/bibref/  

Quantum Mechanics topics are essential building blocks for our understanding of many physical systems. The module assumes basic knowledge in quantum mechanics from modules in earlier years, but will provide a review at the beginning. Topics include a review of quantum mechanics, operator methods and applications to the harmonic oscillator, spin & angular momentum, symmetries in quantum mechanics. The second half of the module then moves beyond isolated quantum systems by addressing the topic of Quantum Entanglement and Quantum Coherence. Here the important concepts of entanglement and decoherence will be studied by developing an understanding of open quantum systems and the density matrix formalism.

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.