MSc — 2024 entry Applied Quantum Computing

Quantum Computing relies on Quantum Physics, naturally, but you do not need to be fully trained as a physicist to be able to produce a quantum computer program. There are, however, some essential but very un-intuitive concepts that you would not normally have met if you have not already become an expert in quantum physics. This module is designed to give an intensive introduction to those concepts. You will not become an expert in quantum physics, but after this module you will understand how to use it for the purpose of processing quantum information.

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.

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.

This module comprises two independent halves, on Financial Derivatives, and Topics in Quantum Mechanics. Financial Derivatives. The first half of this module covers various applications of statistical physics to model share prices and financial markets. This mathematics is then applied to calculating prices for some examples of financial derivatives. Quantum Mechanics topics are essential building blocks for our understanding of many physical systems. The module assumes basic knowledge in quantum mechanics from the Introduction to Quantum Computing, 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.

Quantum Computing relies on Quantum Physics, naturally, but you do not need to be fully trained as a physicist to be able to produce a quantum computer program. There are, however, some essential but very un-intuitive concepts that you would not normally have met if you have not already become an expert in quantum physics. This module is designed to give an intensive introduction to those concepts. You will not become an expert in quantum physics, but after this module you will understand how to use it for the purpose of processing quantum information.

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.  The Python package qiskit is 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 comprises two independent halves, on Quantum Biology (Q Bio) plus Quantum Information and Decoherence (Q Info). 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 the summer project.  Quantum Information and Decoherence. Quantum information theory provides the formalism and mathematical framework needed to design and eventually operate quantum computers. By the end of this half module, a successful student should have a thorough understanding of the application of this formalism to information processing. This will cover the use of key quantum resources such as entanglement and other correlations, as well as a detailed analysis of the process of measurement, including a range of error mitigation techniques. Further to this, the student will learn to model and handle noise in quantum protocols by means of the density matrix formalism and the concept of generalised measurement, as well as the role of irreversibility via the use of master equations. These topics are not typically covered in undergraduate physics courses, and they will expand the theoretical background presented in previous modules (states and operators in Hilbert spaces, uncertainty and commutation relations, measurement and the spectral theorem, the Schrödinger Equation, Dirac notation, etc) which will mostly focus on isolated quantum systems. This half module requires understanding of the basic formalism of QM, either as courses taught in undergraduate physics degrees, or to have taken the module Introduction to Quantum Computing. While the mathematics involved is not too advanced, students will need to have a basic understanding of linear algebra and calculus, as well as familiarity with Dirac notation (Bra-Ket notation).

This module comprises two independent halves, on Quantum Communications (QComm) plus Quantum Simulation (QSim). Quantum communications. This course provides a comprehensive introduction to the principles, protocols, and applications of quantum communications. Students will explore the fundamental concepts of quantum information transmission, quantum cryptography, and quantum communication protocols. They will also examine the challenges, advancements, and real-world applications of quantum communications including both theory and practical aspects. 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 Computing relies on Quantum Physics, naturally, but you do not need to be fully trained as a physicist to be able to produce a quantum computer program. There are, however, some essential but very un-intuitive concepts that you would not normally have met if you have not already become an expert in quantum physics. This module is designed to give an intensive introduction to those concepts. You will not become an expert in quantum physics, but after this module you will understand how to use it for the purpose of processing quantum information.

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.

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.

This module comprises two independent halves, on Financial Derivatives, and Topics in Quantum Mechanics. Financial Derivatives. The first half of this module covers various applications of statistical physics to model share prices and financial markets. This mathematics is then applied to calculating prices for some examples of financial derivatives. Quantum Mechanics topics are essential building blocks for our understanding of many physical systems. The module assumes basic knowledge in quantum mechanics from the Introduction to Quantum Computing, 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.

Quantum Computing relies on Quantum Physics, naturally, but you do not need to be fully trained as a physicist to be able to produce a quantum computer program. There are, however, some essential but very un-intuitive concepts that you would not normally have met if you have not already become an expert in quantum physics. This module is designed to give an intensive introduction to those concepts. You will not become an expert in quantum physics, but after this module you will understand how to use it for the purpose of processing quantum information.

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.  The Python package qiskit is 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 comprises two independent halves, on Quantum Biology (Q Bio) plus Quantum Information and Decoherence (Q Info). 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 the summer project.  Quantum Information and Decoherence. Quantum information theory provides the formalism and mathematical framework needed to design and eventually operate quantum computers. By the end of this half module, a successful student should have a thorough understanding of the application of this formalism to information processing. This will cover the use of key quantum resources such as entanglement and other correlations, as well as a detailed analysis of the process of measurement, including a range of error mitigation techniques. Further to this, the student will learn to model and handle noise in quantum protocols by means of the density matrix formalism and the concept of generalised measurement, as well as the role of irreversibility via the use of master equations. These topics are not typically covered in undergraduate physics courses, and they will expand the theoretical background presented in previous modules (states and operators in Hilbert spaces, uncertainty and commutation relations, measurement and the spectral theorem, the Schrödinger Equation, Dirac notation, etc) which will mostly focus on isolated quantum systems. This half module requires understanding of the basic formalism of QM, either as courses taught in undergraduate physics degrees, or to have taken the module Introduction to Quantum Computing. While the mathematics involved is not too advanced, students will need to have a basic understanding of linear algebra and calculus, as well as familiarity with Dirac notation (Bra-Ket notation).

This module comprises two independent halves, on Quantum Communications (QComm) plus Quantum Simulation (QSim). Quantum communications. This course provides a comprehensive introduction to the principles, protocols, and applications of quantum communications. Students will explore the fundamental concepts of quantum information transmission, quantum cryptography, and quantum communication protocols. They will also examine the challenges, advancements, and real-world applications of quantum communications including both theory and practical aspects. 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.