###### Learning Outcomes:

The module has the following learning outcomes:

- Develop a better understanding of quantum gates and quantum circuit

- Develop a better understanding of quantum algorithms and quantum parallel computation

- Understand the limit of quantum computation due to decoherence

- Understand error correction and the concept of fault tolerant quantum computation

- Ability to understand and simulate quantum circuits

- Ability to understand quantum algorithms

- Introducing of variational quantum algorithms

- Leveraging quantum simulation frameworks to simulate and understand quantum algorithms

###### Indicative Module Content:

The following topics will be covered:

- Review of Quantum Mechanics: state vector and Hilbert space, Dirac notation, matrix mechanics, unitary Hermitian operators, time evolution through exponentiation)

- Review of Quantum Computing: single qubit quantum gate, quantum gates, quantum teleportation and Bell pair production, measurement

- Introduction to the Density Operator: density matrix vs. state vector, properties of density operator, pure and mixed states

- Time Evolution of Density Operators: Von Neumann equation, Lindblad equation, the concept of open quantum systems

- Noisy Quantum Computing: errors, decoherence

- Errors in Open Quantum Systems: quantum channels, amplitude-damping, phase-damping, depolarisation

- Quantum Information and Entropies: comparison with classical Information, Von Neumann entropy and derivatives, mutual information

- Error Suppression and Mitigation Strategies: error mitigation, error correction techniques, repetition code

- Canonical Quantum Algorithms: Quantum Fourier Transform (QFT), Shor’s Algorithm, Grover Search Algorithm

- Invited guest lectures on research in quantum computing