Mathematics for Quantum Informatics
Code | Completion | Credits | Range | Language |
---|---|---|---|---|
QNI-MQI | Z,ZK | 6 | 2P+2C | English |
- Course guarantor:
- Štěpán Starosta
- Lecturer:
- Štěpán Starosta
- Tutor:
- Štěpán Starosta
- Supervisor:
- Department of Applied Mathematics
- Synopsis:
-
Linear algebra on finite dimensional spaces with scalar product, Hilbert spaces, Dirac's bra-ket formalism, normal, Hermitian and unitary operators, operator spectrum, orthonormalization, diagonalization, matrix exponential, tensor product of vector spaces and operators. Discrete Fourier transform and fast Fourier transform.
- Requirements:
- Syllabus of lectures:
-
1. Complex numbers, vector spaces, scalar product.
2. Geometry of Hilbert spaces of finite dimension: orthonormal basis, Fourier expansion, Parseval's equality, Schwarz inequality.
3. Linear operators on Hilbert spaces of finite dimension, matrix representation of an operator.
4. Dirac bra-ket notation. Hermitian adjoint operator (matrix). Dual space of a Hilbert space of finite dimension and Riesz theorem.
5. Eigenvalues and eigenvectors of an operator (matrix), spectrum of an operator, diagonalization.
6. Normal operators: projectors, Hermitian operators, unitary operators.
7. Properties of normal operators, especially spectral properties. Spectral decomposition of operators (matrices).
8. Tensor product of vector spaces and operators (matrices).
9. Matrix exponential, relation of Hermitian and unitary operators (matrices).
10. Operator trace and its properties. Density matrix.
11. Discrete Fourier Transform (DFT) as a unitary operator (matrix).
12. Properties of Discrete Fourier transforms.
13. DFT implementation using Fast Fourier Transform (FFT).
- Syllabus of tutorials:
-
1. Complex numbers, vector spaces, scalar product.
2. Geometry of Hilbert spaces of finite dimension: orthonormal basis, Fourier expansion, Parseval's equality, Schwarz inequality.
3. Linear operators on Hilbert spaces of finite dimension, matrix representation of an operator.
4. Dirac bra-ket notation. Hermitian adjoint operator (matrix). Dual space of a Hilbert space of finite dimension and Riesz theorem.
5. Eigenvalues and eigenvectors of an operator (matrix), spectrum of an operator, diagonalization.
6. Normal operators: projectors, Hermitian operators, unitary operators.
7. Properties of normal operators, especially spectral properties. Spectral decomposition of operators (matrices).
8. Tensor product of vector spaces and operators (matrices).
9. Matrix exponential, relation of Hermitian and unitary operators (matrices).
10. Operator trace and its properties. Density matrix.
11. Discrete Fourier Transform (DFT) as a unitary operator (matrix).
12. Properties of Discrete Fourier transforms.
13. DFT implementation using Fast Fourier Transform (FFT).
- Study Objective:
- Study materials:
-
1. Strang, G.: Introduction to Linear Algebra, 5th Edition
Wellesley-Cambridge Press 2016, ISBN 978-0980232776
2. Lay, D.C., Lay S. R., McDonald, J. J.: Linear Algebra and Its Applications, 5th Edition, Pearson 2015, ISBN 978-0321982384
3. Lipton, R. J., Regan, K. W.: Introduction to Quantum Algorithms via Linear Algebra, 2nd Edition, MIT Press 2021, ISBN 9780262045254
- Note:
-
Information about the course and teaching materials can be found at https://courses.fit.cvut.cz/QNI-MQI
- Further information:
- https://courses.fit.cvut.cz/QNI-MQI
- Time-table for winter semester 2025/2026:
- Time-table is not available yet
- Time-table for summer semester 2025/2026:
- Time-table is not available yet
- The course is a part of the following study plans:
-
- Quantum Informatics (compulsory course in the program)