Publications

Order by: publication date number of citations

• kdotpy: k·p theory on a lattice for simulating semiconductor band structures

  Wouter Beugeling, Florian Bayer, Christian Berger, Jan Böttcher, Leonid Bovkun, Christopher Fuchs, Maximilian Hofer, Saquib Shamim, Moritz Siebert, Li-Xian Wang, Ewelina M. Hankiewicz, Tobias Kießling, Hartmut Buhmann, Laurens W. Molenkamp

  SciPost Phys. Codebases 47 (2025) · published 28 January 2025 | Toggle abstract · pdf

  The software project kdotpy provides a Python application for simulating electronic band structures of semiconductor devices with k·p theory on a lattice. The application implements the widely used Kane model, capable of reliable predictions of transport and optical properties for a large variety of topological and non-topological materials with a zincblende crystal structure. The application automates the tedious steps of simulating band structures. The user inputs the relevant physical parameters on the command line, for example materials and dimensions of the device, magnetic field, and temperature. The program constructs the appropriate matrix Hamiltonian on a discretized lattice of spatial coordinates and diagonalizes it. The physical observables are extracted from the eigenvalues and eigenvectors and saved as output. The program is highly customizable with a large set of configuration options and material parameters.

  4 citations

• Codebase release 1.0 for kdotpy

  Wouter Beugeling, Florian Bayer, Christian Berger, Jan Böttcher, Leonid Bovkun, Christopher Fuchs, Maximilian Hofer, Saquib Shamim, Moritz Siebert, Li-Xian Wang, Ewelina M. Hankiewicz, Tobias Kießling, Hartmut Buhmann, Laurens W. Molenkamp

  SciPost Phys. Codebases 47-r1.0 (2025) · published 28 January 2025 | Toggle abstract · src

  The software project kdotpy provides a Python application for simulating electronic band structures of semiconductor devices with k·p theory on a lattice. The application implements the widely used Kane model, capable of reliable predictions of transport and optical properties for a large variety of topological and non-topological materials with a zincblende crystal structure. The application automates the tedious steps of simulating band structures. The user inputs the relevant physical parameters on the command line, for example materials and dimensions of the device, magnetic field, and temperature. The program constructs the appropriate matrix Hamiltonian on a discretized lattice of spatial coordinates and diagonalizes it. The physical observables are extracted from the eigenvalues and eigenvectors and saved as output. The program is highly customizable with a large set of configuration options and material parameters.

  1 citation

• Codebase release 1.1 for QDsim

  Valentina Gualtieri, Charles Renshaw-Whitman, Vinicius Hernandes, Eliska Greplova

  SciPost Phys. Codebases 46-r1.1 (2025) · published 23 January 2025 | Toggle abstract · src

  We introduce QDsim, a Python package tailored for the rapid generation of charge stability diagrams in large-scale quantum dot devices, extending beyond traditional double or triple dots. QDsim is founded on the constant interaction model from which we rephrase the task of finding the lowest energy charge configuration as a convex optimization problem. Therefore, we can leverage the existing package CVXPY, in combination with an appropriate powerful solver, for the convex optimization which streamlines the creation of stability diagrams and polytopes. Through multiple examples, we demonstrate how QDsim enables the generation of large-scale dataset that can serve a basis for the training of machine-learning models for automated tuning algorithms. While the package currently does not support quantum effects beyond the constant interaction model, QDsim is a tool that directly addresses the critical need for cost-effective and expeditious data acquisition for better tuning algorithms in order to accelerate the development of semiconductor quantum devices.

  1 citation

• QDsim: A user-friendly toolbox for simulating large-scale quantum dot devices

  Valentina Gualtieri, Charles Renshaw-Whitman, Vinicius Hernandes, Eliska Greplova

  SciPost Phys. Codebases 46 (2025) · published 23 January 2025 | Toggle abstract · pdf

  We introduce QDsim, a Python package tailored for the rapid generation of charge stability diagrams in large-scale quantum dot devices, extending beyond traditional double or triple dots. QDsim is founded on the constant interaction model from which we rephrase the task of finding the lowest energy charge configuration as a convex optimization problem. Therefore, we can leverage the existing package CVXPY, in combination with an appropriate powerful solver, for the convex optimization which streamlines the creation of stability diagrams and polytopes. Through multiple examples, we demonstrate how QDsim enables the generation of large-scale dataset that can serve a basis for the training of machine-learning models for automated tuning algorithms. While the package currently does not support quantum effects beyond the constant interaction model, QDsim is a tool that directly addresses the critical need for cost-effective and expeditious data acquisition for better tuning algorithms in order to accelerate the development of semiconductor quantum devices.

  1 citation

• Consistent, multidimensional differential histogramming and summary statistics with YODA 2

  Andy Buckley, Louie Corpe, Matthew Filipovich, Christian Gutschow, Nick Rozinsky, Simon Thor, Yoran Yeh, Jamie Yellen

  SciPost Phys. Codebases 45 (2025) · published 16 January 2025 | Toggle abstract · pdf

  Histogramming is often taken for granted, but the power and compactness of partially aggregated, multidimensional summary statistics, and their fundamental connection to differential and integral calculus make them formidable statistical objects, especially when very large data volumes are involved. But expressing these concepts robustly and efficiently in high-dimensional parameter spaces and for large data samples is a highly non-trivial challenge – doubly so if the resulting library is to remain usable by scientists as opposed to software engineers. In this paper we summarise the core principles required for consistent generalised histogramming, and use them to motivate the design principles and implementation mechanics of the re-engineered YODA histogramming library, a key component of physics data–model comparison and statistical interpretation in collider physics.

  4 citations

• Codebase release 2.0 for YODA

  Andy Buckley, Louie Corpe, Matthew Filipovich, Christian Gutschow, Nick Rozinsky, Simon Thor, Yoran Yeh, Jamie Yellen

  SciPost Phys. Codebases 45-r2.0 (2025) · published 16 January 2025 | Toggle abstract · src

  Histogramming is often taken for granted, but the power and compactness of partially aggregated, multidimensional summary statistics, and their fundamental connection to differential and integral calculus make them formidable statistical objects, especially when very large data volumes are involved. But expressing these concepts robustly and efficiently in high-dimensional parameter spaces and for large data samples is a highly non-trivial challenge – doubly so if the resulting library is to remain usable by scientists as opposed to software engineers. In this paper we summarise the core principles required for consistent generalised histogramming, and use them to motivate the design principles and implementation mechanics of the re-engineered YODA histogramming library, a key component of physics data–model comparison and statistical interpretation in collider physics.

  2 citations

• Codebase release 1.0 for EMRI_MC

  Ippocratis D. Saltas, Roberto Oliveri

  SciPost Phys. Codebases 44-r1.0 (2025) · published 16 January 2025 | Toggle abstract · src

  We describe a simple and efficient Python code to perform Bayesian forecasting for gravitational waves (GW) produced by Extreme-Mass-Ratio-Inspiral systems (EMRIs). The code runs on GPUs for an efficient parallelised computation of thousands of waveforms and sampling of the posterior through a Markov-Chain-Monte-Carlo (MCMC) algorithm. EMRI_MC generates EMRI waveforms based on the so-called kludge scheme, and propagates it to the observer accounting for cosmological effects in the observed waveform due to modified gravity/dark energy. The code provides a helpful resource for forecasts for interferometry missions in the milli-Hz scale, e.g the satellite-mission LISA.

  1 citation

• EMRI_MC: A GPU-based code for Bayesian inference of EMRI waveforms

  Ippocratis D. Saltas, Roberto Oliveri

  SciPost Phys. Codebases 44 (2025) · published 16 January 2025 | Toggle abstract · pdf

  We describe a simple and efficient Python code to perform Bayesian forecasting for gravitational waves (GW) produced by Extreme-Mass-Ratio-Inspiral systems (EMRIs). The code runs on GPUs for an efficient parallelised computation of thousands of waveforms and sampling of the posterior through a Markov-Chain-Monte-Carlo (MCMC) algorithm. EMRI_MC generates EMRI waveforms based on the so-called kludge scheme, and propagates it to the observer accounting for cosmological effects in the observed waveform due to modified gravity/dark energy. The code provides a helpful resource for forecasts for interferometry missions in the milli-Hz scale, e.g the satellite-mission LISA.

  2 citations

• Codebase release 1.0 for QDarts

  Jan A. Krzywda, Weikun Liu, Evert van Nieuwenburg, Oswin Krause

  SciPost Phys. Codebases 43-r1.0 (2025) · published 9 January 2025 | Toggle abstract · src

  We present QDarts, an efficient simulator for realistic charge stability diagrams of quantum dot array (QDA) devices in equilibrium states. It allows for pinpointing the location of concrete charge states and their transitions in a high-dimensional voltage space (via arbitrary two-dimensional cuts through it), and includes effects of finite tunnel coupling, non-constant charging energy and a simulation of noisy sensor dots. These features enable close matching of various experimental results in the literature, and the package hence provides a flexible tool for testing QDA experiments, as well as opening the avenue for developing new methods of device tuning.

  1 citation

• QDarts: A quantum dot array transition simulator for finding charge transitions in the presence of finite tunnel couplings, non-constant charging energies and sensor dots

  Jan A. Krzywda, Weikun Liu, Evert van Nieuwenburg, Oswin Krause

  SciPost Phys. Codebases 43 (2025) · published 9 January 2025 | Toggle abstract · pdf

  We present QDarts, an efficient simulator for realistic charge stability diagrams of quantum dot array (QDA) devices in equilibrium states. It allows for pinpointing the location of concrete charge states and their transitions in a high-dimensional voltage space (via arbitrary two-dimensional cuts through it), and includes effects of finite tunnel coupling, non-constant charging energy and a simulation of noisy sensor dots. These features enable close matching of various experimental results in the literature, and the package hence provides a flexible tool for testing QDA experiments, as well as opening the avenue for developing new methods of device tuning.

  1 citation