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| Detailed information |
| Original study plan |
Master's programme Computer Science 2025W |
| Learning Outcomes |
Competences |
| The students understand the modern foundations of quantum computing. This includes the quantum circuit model, its mathematical description in terms of matrices and vectors, as well as some of the most prevalent and celebrated quantum algorithms: amplitude estimation (aka Grover's algorithm), the quantum Fourier transform, quantum phase estimation and Shor's algorithm for factoring products of distinct prime numbers. The students are able to to apply these concepts to simple case studies.
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| Skills |
Knowledge |
After completion, students know how to
- formulate a quantum processing unit as a pipeline with a classical binary input, a reversible quantum logic part at the center and a classical binary output (K2, K3, K4)
- read, interpret and creatively use the quantum circuit model to completely specify a given quantum computation (K2, K3, K4)
- translate quantum circuits into (exponentially larger) matrix vector multiplications (K3)
- use formalism and math to analyze prevalent quantum algorithms. This includes an in-depth understanding of amplitude estimation, quantum phase estimation, and Shor's factoring algorithm (K4, K5)
- respect the limitations of quantum computing (K2, K5)
- debunk prevalent misconceptions about the power and limitations of quantum computers (K2, K5)
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- quantum circuits
- matrix-vector representations thereof
- randomized readout and how to deal with it
- entanglement
- amplitude estimation
- quantum Fourier transform
- quantum phase estimation
- Shor's algorithm
- hybrid quantum-classical approaches to learning quantum phenomena
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| Criteria for evaluation |
written exam
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| Language |
English |
| Study material |
lecture notes (latex)
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| Changing subject? |
No |
| Further information |
This lecture and the accompanying exercise classes form two pillars of a larger, didactic vision.
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