Applied Quantum Algorithms (Graduate)
  • Quantum computing is an upcoming technology which is expected to have game-changing applications in all computationally-intensive branches of the natural sciences; material sciences, chemistry and physics. Resource-efficient algorithm design is an essential step towards making quantum computers viable for these applications. This is an advanced course for students with understanding of the basics of quantum computing (e.g. for students which have taken Quantum Algorithms at LIACS or Quantum Information at LION), that emphasises practical quantum computing for potential near-term applications. In this course, you will learn about modern quantum algorithmic techniques, and how they are applied in quantum chemistry, quantum many-body physics and machine learning. Furthermore, you will learn about the fundamentals of quantum error correction and error mitigation techniques required to make noisy quantum computers functional.

Building Blocks of Quantum Matter (Undergraduate)
  • The Universe is both light and dark. In this course, we focus on the light and what its spectral content can teach us about the elemental and molecular composition of luminous objects (stars, clouds, lamps, etc.) in space and on Earth. In order to answer this question we have to go well beyond what was taught in the Quantum Mechanics courses about the energy-level structure of the Hydrogen atom and learn about multi-electron atoms and simple molecules. Experimental data will guide us in the development of the theoretical concepts and models. We will discuss the very important role of symmetries, in particular in molecules.

Quantum algorithms (graduate)
  • Entanglement, nonlocality and implications.

Quantum Entrepreneurship Laboratory (graduate)
  • Overview of quantum algorithms. Classical/Quantum models of computation. Computability and complexity classes. Basic quantum algorithms (Deutsch-Jozsa, Simon's, Quantum Fourier Transform, Shor's; other algorithms, Grover's, sampling). Ground state preparation and combinatorial optimization. Hamiltonians and ground states. Basic algorithms for ground state preparation, Quantum Phase Estimation, Adiabatic Algorithm. Quantum Linear Algebra, Linear Combination of Unitaries Lemma. Approximate Ground State projectors.

Quantum algorithms (graduate)
  • Ground state preparation. Hamiltonians and ground states. Rudimentary algorithms for ground state preparation. Quantum Phase Estimation. Adiabatic Algorithms. LCU lemma. Approximate Ground State Projectors.

PhD students
  • In the Teaching section of my CV you can find information about previous teaching activities I carried out.