Maximilian Balthasar Mansky, M. Sc.

Maximilian Balthasar Mansky, M. Sc.

Lehrstuhl für Mobile und Verteilte Systeme

Ludwig-Maximilians-Universität München, Institut für Informatik

Oettingenstraße 67
80538 München

Raum E004

Telefon: +49 89 / 2180-9155

Mobile: +49 179 5391026

Fax: +49 89 / 2180-9148


Research Interests

  • Theoretical foundations of quantum computing
  • Mathematical structures of QC
  • Circuit length optimizations

Bachelor/Master thesis

If you are interested in a topic that aligns with my own research interests, please reach out to me. You can also pick a topic from the list below:

  • Efficient universal gate sets for SU(4) and higher dimensions: Based on the work of Harrow et al, „Efficient discrete approximations of quantum gates“, extend their ideas to higher dimensions. You don’t need a strong math background for this thesis.
  • Black-Scholes on Quantum. It is fairly well known that there is a direct correspondence between the Black-Scholes equation (modeling of prices of Options on stocks) and physical models, for example for Heat transfer. Less well known is the fact that the underlying differential equation is the same as the Schrödinger equation, just without the imaginary factor. Can this fact be used to map Black-Scholes/Heat models to a quantum computer? Some prior exposure to Black-Scholes/Heat model (Green’s equation) is beneficial, you should be comfortable with differential equations beyond a conceptual level.
  • Topology of quantum machine learning. There is a fundamental difference between classical machine learning on ℝⁿ and its counterpart quantum ML on SU(2ⁿ). Beyond the larger state space, all curves (enclosing areas of classification) are closed – And stay closed when moving backwards through the layers of the neural network. In contrast, in classical machine learning open stays open. In this thesis, the difference and consequences thereof are to be examined. You should be at least be interested in the area of topology (Key words: push-forward; pull-back, Homomorphisms) and be somewhat with how machine learning works conceptually.