Data and analysis scripts for paper "Interferometric Single-Shot Parity Measurement in an InAs-Al Hybrid Device"
This repository contains parity readout simulation code and scripts for generating data-driven figures for the published paper "Interferometric Single-Shot Parity Measurement in InAs-Al Hybrid Devices" by Microsoft Azure Quantum.
- Preprint: arXiv:2401.09549
- Peer-reviewed publication: Nature 638, 651–655 (2025).
All references to figure numbers in this repository refer to the peer-reviewed revision of the paper published in Nature which may differ from the preprint.
If you are interested in generating the plots in the paper with minimal interaction with this repo, look at the sections Requirements, Minimal Data to Reproduce Figures and Minimal Code for Reproducing Measured Figures.
Table of Contents
- Requirements
- Data
- Reproducing Figures in the Paper
- Modules
- Microsoft Open Source Code of Conduct
- Trademarks
The data analysis and figure generating scripts in this repository were run using Python 3.10.9 and the environment specified in uv.lock (we also provide a requirements.txt for advanced users
who do not wish to use uv).
The simulation data and figures were generated using Julia v1.10.8. See ParityReadoutSimulator/README.md for additional information on the ParityReadoutSimulator and Julia environment requirements.
The quickest way to reproduce the environment used for data analysis and figure generation is to use the
tool uv, which can be installed by following these instructions.
Once uv is installed, running the following commands will install the necessary software into an isolated
environment, and activate that environment for future use:
uv sync # download Python 3.10.9, and all dependencies, creating a virtual environment with everything installed
source .venv/bin/activate # activate the virtual environmentMeasured and simulated data is available in the accompanying dataset on Zenodo. You can download it manually and point to the data folder in paths.py, see Data Paths for more info.
For convenience, you can also run the prepare_data.py python script, which will download the data from Zenodo
and put it in a folder called data.
As an interface for all our code, all raw and generated data are organized into the following folder variables:
DATA_FOLDER: Variable for a folder containing four subdirectories outlined belowRAW_DATA_FOLDER: Variable for a folder containing all measured data to be used for analysis and plotting.CONVERTED_DATA_FOLDER: Variable for a target folder to contain all CQ Converted datasets generated from runningcq_conversion/workflow.pyon the data inRAW_DATA_FOLDERCQ_CONV_INTERMEDIATE_FIGURE_FOLDER: Variable for a target folder to store intermediate figures for debugging the output of various stages of CQ ConversionSIMULATION_DATA_FOLDER: Variable for a folder containing all the simulated data.
The paths to these folder variables are defined in paths.py. By default, DATA_FOLDER points to
a folder named data in the root of this repository. These variables are propagated through the various
scripts that rely on measured, simulated or CQ converted data.
We provided a python script prepare_data.py that (optionally) downloads the data from zenodo
and automatically generates this target directory structure. When you execute this script, you will have the option to
download all our datasets (~150Gb), a minimal subset of datasets to reproduce the plots in the paper (~6GB) or manually
download the data. After that the script will unpack the zip files into their target directory.
In case this fails (e.g. due to a different OS), the zip outputs on the
zenodo should correspond to the following directories:
-
simulated.zip$\to$ data/simulated/... -
converted_data.zip$\to$ data/converted_data/... -
dot_tuneup_A1.zip$\to$ data/raw_data/dot_tuneup_A1/... -
injector.zip$\to$ data/raw_data/injector/... -
mpr_A1.zip$\to$ data/raw_data/mpr_A1/... -
mpr_A2.zip$\to$ data/raw_data/mpr_A2/... -
mpr_B1.zip$\to$ data/raw_data/mpr_B1/... -
charge_noise.zip$\to$ data/raw_data/charge_noise/... -
cut_loop_A.zip$\to$ data/raw_data/cut_loop_A/... -
cut_loop_B.zip$\to$ data/raw_data/cut_loop_B/... -
qdmzm_A1.zip$\to$ data/raw_data/qdmzm_A1/... -
qpp.zip$\to$ data/raw_data/qpp/... -
tgp2_tuneup.zip$\to$ data/raw_data/tgp2_tuneup/... -
thermometry.zip$\to$ data/raw_data/thermometry/... -
trivial_A.zip$\to$ data/raw_data/trivial_A/... -
trivial_B.zip$\to$ data/raw_data/trivial_B/...
Note: All the analysis scripts in measured_analysis_figures use only the files in data/converted_data and data/simulated
except for figureS6_dot_tuneup.md, figureS7_tgp2_tuneup.md and figureS18_deviceB1_charge_noise.md which only use raw measured data.
Some of the raw datasets (all datasets labelled run_name.zip) contain timetraces that were left uncoarsened and as a result, are really large. If you are only interested
in using the coarsened data to regenerate a figure, you can download the converted_data.zip file from zenodo. This zip contains all the CQ converted RF measurements used to
generate the figures (see note in previous subsection
for more details).
As a result, the minimal datasets you need to download from Zenodo to reproduce all figures in the paper are:
simulated.zip(1.0 GB)converted_data.zip(4.8 GB)charge_noise.zip(3.0 MB)dot_tuneup_A1.zip(0.8 MB)tgp2_tuneup.zip(20.1 MB)
If downloaded manually, you should put these files in a directory named data and then run prepare_data.py to extract the files into their respective locations automatically.
If you are only interested in reproducing the figures, you need to follow the instructions in Requirements
and downloading the minimal datasets when asked by prepare_data.py (i.e. run python prepare_data.py --download-minimal).
Now that the data and environment are ready, you can run jupytext --execute measured_analysis_figures/figure*.md
To reproduce simulation figures, follow the instructions in ParityReadoutSimulator/README.md to setup the environment. You should then be able to reproduce the simulated figures and reference datasets by running through the notebooks manually. You can also do the following to automate the generation of all of the below.
For more advanced users, refer to the below section on the various Modules in this repo.
This package has 4 main folders:
-
cq_conversion: A python module responsible for converting measured data from units of voltage to units of (quantum) capacitance ($C_{\rm Q}$ )provided information about the resonator and RF background. -
measured_analysis_figures: A folder of python scripts and notebooks that are used to generate all data analysis figures from measured data. Requires data that has already been$C_{\rm Q}$ -converted, either by using the$C_{\rm Q}$ -converted data from the accompanying dataset or by running the$C_{\rm Q}$ -conversion code as outlined in the CQ Conversion section of this document on the raw measured data. -
ParityReadoutSimulator: A Julia package for computing the steady-state capacitive response of small mesoscopic devices composed of single-level quantum dots and Majorana zero-modes. Also contains the notebooks used to generate reference simulated data and figures. -
thermometry: A python modules responsible for extracting the electron temperature from DQD datasets acquired at various different temperatures by curve_fitting correlated parameters together.
This module contains all the datasets that need to be CQ converted in datasets.py along with information about the resonator and background.
To run CQ conversion on a specific datasets (for example the A1 and thermometry datasets), run python cq_conversion/workflow.py A1 thermometry in your terminal. To run CQ conversion on all
datasets, use python cq_conversion/workflow.py --all. This takes long for datasets with timetraces since they are large hypercubes - this could easily take over an hour on slower machines due to the size of the timetrace datasets.
The workflow file reads netcdfs from RAW_DATA_FOLDER and puts them in CONVERTED_DATA_FOLDER for use in later analyses and plotting.
Other files:
corrections.py: Applies required correction on the measured data for off-resonance drive, required for the small parameter expansion used to cq convert, see the CQ conversion supplementary for more details.cq_conversion.py: The logic for interpolating a calibration curve and assigning the capacitance value based on the proximity of the measured data to the calibration curve.cq_plot.py: Plotting code required to generate intermediate cq conversion figures. These figures aren't included the paper, they are meant as useful sanity checks when performing CQ conversion.extract_parameters.py: A script that spits out input parameters to use for CQ conversion if there is no prior for this information - can be run in the same manner asworkflow.py. E.g.python extract_parameters.py --all.helpers.py: Helper functions that are useful when dealing with calibration curves.
Code that is used to generate figures 3, S5, S6, S7, S9, S10, S11, S12, S13, S14, S16, S17, S18 and S19 in the Nature paper "Interferometric Single-Shot Parity Measurement in InAs-Al Hybrid Devices".
analysis_code.timetrace_analysis.py: Defines functions used for time trace analysis and metric extraction (kurtosis, dwell times).analysis_code.plotting_code.py: Plotting functions called frommeasurement_analysis_figuresnotebooks.analysis_code/common.py: Helper plotting tools.
To regenerate all figures in the paper with measured data, you can run jupytext --execute measured_analysis_figures/*.md. You can alternatively convert a script to a python notebook you can run using jupytext measured_analysis_figures/[NAME].md --to ipynb, then run through it as you normally would in Jupyter Notebook or VSCode. See the jupytext documentation for more details on how to work with md files.
The notebooks figureS3_deviceA1_parity_measurements.md, figureS9_deviceA2_parity_measurements.md, figureS10_deviceB1_parity_measurements.md,
figureS11_qd_mzm.md, figureS12_scenarios.md, figureS13_cut_loop.md, figureS14_trivial_measurement.md,
figureS16_deviceB_qpp_times.md, figureS17_qpp.md, figureS19_deviceB_thermometry.md additionally requires CQ Converted data be generated into the CONVERTED_DATA_FOLDER.
You can either use the cq converted data from zenodo or run the CQ conversion code as outlined in the CQ Conversion section of this document on the raw data from zenodo.
The notebook figureS19_deviceB_thermometry.md requires a simulation reference dataset generated from ParityReadoutSimulator in addition to the cq converted measurement data. You can either use the reference dataset provided in the zenodo upload or regenerate the simulation data by following the details in the ParityReadoutSimulator section.
A Julia package for computing the steady-state capacitive response of small mesoscopic devices composed of single-level quantum dots and a few Majorana zero-modes. This simulation code follows the methods and assumptions outlined in section "S2.4. Dynamical
ParityReadouSimulator/examples contains notebooks for generating figures based on the accompanying simulated data. Each notebook also contains the required code to regenerate the simulation data using the ParityReadoutSimulator package.
A module used in figureS19_deviceB_thermometry.md to perform simultaneous curve fits of double quantum dots (DQD) measurements acquired at the same temperature. Compares open systems dynamics simulations from
ParityReadoutSimulator to CQ converted measured data to identify and extract the broadening caused by temperature.
This module relies on a modified variant of the hough lines algorithm to detect charge resonances in a DQD, to extract a group of correlated peaks that lie on the same charge transition. We assume that these curves share a dot-dot coupling and broadening term.
These correlated fits allows to fit one temperature for every gate-gate map measured
(acquired at the same puck temperature), multiple broadening terms and dot-dot couplings, one per extracted line and mutliple shifts, 3 per each curve.
The shifts are one
See Supplementary "S10: Electron temperature" for more details on this analysis.
This project has adopted the Microsoft Open Source Code of Conduct.
Resources:
- Microsoft Open Source Code of Conduct
- Microsoft Code of Conduct FAQ
- Contact [email protected] with questions or concerns
- Employees can reach out at aka.ms/opensource/moderation-support
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.