The 2023 list of quantum device simulation

Introduction

With all this fuss about quantum things, I think it’s high time I updated the list of simulators post I did some years ago. I am completely remaking this post, instead of adding to the previous, because now things are more clear to me and to the community at large where each subdomain lies. Since this will be a list from my own experience, feel free to contact me with the software that you would like to see added in this list.

Also, it’s a list for technologies that can host Quantum Information carriers based on solid state device types, that is: semiconducting, superconducting, or even photonic. That is, with the possibility of inclusion of materials science and effects like topological. It is not about trapped ions and optical lattices, although apparently, you can do that too with Wannier functions, a method I will mention here.

Materials

In any type of solid state simulation, the materials play a fundamental role. However, we don’t always have to do some quantum mechanics simulation for different materials in order to do a simulation of the final device. This is because many of the effects get ‘wrapped up’ in simple variables, or do not affect the performance of the device.

Automating device data analysis

Lately, there have been some efforts to incorporate machine learning in experimental measurements, which are generally quite known in the community, and especially the quantum one (see here for example). While these types of work are currently ‘hot’, I decided to do a small post here about the small cousin of ML, which is automation. That is: Extracting information from large datasets of experiments.

This came about from my recently published work done at Grenoble, in which I had the chance to work with a large number of well-organized experiments. And I think it goes nicely with my previous post which is about automation in materials simulation.

Here, instead, I will present some common methods of extracting pinch-off voltages using Python. I did a previous post on a similar subject. Together they can be quite handy for extracting information fast from 1D data. Of course, they can be generalized for 2D also, but the here we focus on device measurements and not spectroscopy. In fact, for the 2D plots I analysed, I handled them as a list of 1D data, so I applied immediately similar routines, instead of 2D ones.