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David Wierichs

dwierichs(he/him)

Quantum Scientist - Software

Xanadu

I like to think about differentiation and representations of quantum programs, and I enjoy coding up research ideas and useful features for anyone to use in PennyLane.


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Contributions13

About

I am a scientist & software developer at Xanadu, before that I did my PhD in physics at the university of Cologne, Germany (See my thesis on gradient estimation and gradient-based optimization of parametrized quantum circuits). I studied physics in Bielefeld, Aachen, and Munich (all in Germany).

Here are some demos/how-tos/tutorials/blog posts I wrote:

  • Demo: Optimal control for gate compilation
    Shows compilation of a gate into a pulse sequence using differentiable pulse programming.
  • Demo: Generalized parameter-shift rules
    Discusses a generalization of the popular "parameter-shift rule" to more general gates, based on a paper of ours.
  • Demo: Here comes the SU(N): Multivariate quantum gates and gradients
    Showcases arbitrary special unitary gates as circuit ansätze, and their differentiation, based on the eponymous paper.
  • Demo: Quantum Analytic Descent
    With Elies Gil-Fuster. Implements and discusses the eponymous algorithm from this paper.
  • Demo: Optimization Using SPSA
    With Antal Szava. Showcases how stochastic gradient estimation can help with convergence (speed).
  • Demo: Training and evaluating quantum kernels
    With Elies Gil-Fuster, Johannes Jakob Meyer, Peter-Jan Derks, and Tom Hubregtsen. Explores trainable quantum embedding kernels for near-term quantum hardware, based on a QHack-originated paper (PRA).
  • How to collect statistics of mid-circuit measurements.
  • How to create dynamic circuits with mid-circuit measurements.
  • Tutorial: Introduction to mid-circuit measurements

dwierichs joined the PennyLane Community on 2023/09/25.

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PennyLane is an open-source software framework for quantum machine learning, quantum chemistry, and quantum computing, with the ability to run on all hardware. Built with ❤️ by Xanadu.

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