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

TL;DR

QDsim is a Python toolbox that efficiently generates charge stability diagrams for large-scale quantum dot devices, facilitating data-driven tuning and analysis in quantum electronics.

Contribution

It introduces a convex optimization-based approach using CVXPY to rapidly produce stability diagrams for large quantum dot arrays, extending beyond traditional small systems.

Findings

Enables large-scale dataset generation for machine learning

Streamlines stability diagram creation with convex optimization

Supports rapid data acquisition for quantum device tuning

Abstract

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.