Module overview
Students should be able to follow and appreciate the key concepts underpinning quantum algorithmic information processing, including the encoding, transformation and measurement of quantum state. They will be able to write programs using specialist libraries that create quantum circuit layouts. They will be able to understand the fundamental difference between quantum and classical computing.
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Trace the complexity of quantum algorithms to appreciate the notion of quantum supremacy.
- Ability to translate mathematical descriptions into quantum circuits and program their layout in specialist libraries
- Implement and clarify protocols that perform specific algorithmic tasks, while confronting the issues that illustrate the particular challenges of explaining the behaviour of the physical substrate in classical terms
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Understand the key concepts of unitary state evolution; quantum entanglement; projective measurement outcomes
- Understand why quantum state maintenance is fragile and requires quantum error correction
- Understand key distinguishing characteristics–between classical (bit) and quantum (qubit) information; between physical and computational state, fundamental to unconventional computing paradigms
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Having successfully completed the module, you will be able to demonstrate the ability to track the evolution of states and their measurements using linear algebraic methods in specialist software environments
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Presentation skills in conveying technical and conceptually challenging ideas in verbal and written forms, backed up with mathematical arguments or code-based experiments
Syllabus
Qubits, unitary operators and measurements
Quantum logic gates and circuits; reversible computation
Quantum superposition and entanglement; no cloning theorem
EPR paradox; Bell states; GHZ states; quantum teleportation
Programming quantum circuits
Deutsch-Josza algorithm, Grover's search algorithm
Quantum factoring -- quantum Fourier transform and Shor's algorithm
Quantum error correction, stabiliser codes
Adiabatic, measurement-based and topological quantum computation
Quantum cryptographic key distribution, BB84
Quantum information theory
Noisy Intermediate Scale Quantum computing: Quantum machine learning
Learning and Teaching
Teaching and learning methods
Assessment will be based on exam and coursework (50/50) -- reports and in-class presentations on specific topics.
Referrals and repeats by examination only.
| Type | Hours |
|---|---|
| Assessment tasks | 50 |
| Wider reading or practice | 44 |
| Lecture | 36 |
| Specialist Laboratory | 20 |
| Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Analysis and report | 50% |
| Exam | 50% |