9 citations · 13 across the 2 of their papers we have counts for
6 papers
Automated tuning of double quantum dots into specific charge states using neural networks
Renato Durrer, Benedikt Kratochwil, Jonne V. Koski +5
While quantum dots are at the forefront of quantum device technology, tuning multi-dot systems requires a lengthy experimental process as multiple parameters need to be accurately…
Fully automated identification of 2D material samples
Eliska Greplova, Carolin Gold, Benedikt Kratochwil +7
Thin nanomaterials are key constituents of modern quantum technologies and materials research. Identifying specimens of these materials with properties required for the development…
Unsupervised identification of topological order using predictive models
Eliska Greplova, Agnes Valenti, Gregor Boschung +3
Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover ne…
Hamiltonian Learning for Quantum Error Correction
Agnes Valenti, Evert van Nieuwenburg, Sebastian Huber +1
The efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use-cases the precise engineering of a Hamiltonian is required b…
Quantum parameter estimation with a neural network
Eliska Greplova, Christian Kraglund Andersen, Klaus Mølmer
We propose to use neural networks to estimate the rates of coherent and incoherent processes in quantum systems from continuous measurement records. In particular, we adapt an imag…
Conditioned spin and charge dynamics of a single electron quantum dot
Eliska Greplova, Edward A. Laird, G. Andrew D. Briggs +1
In this article we describe the incoherent and coherent spin and charge dynamics of a single electron quantum dot. We use a stochastic master equation to model the state of the sys…