Data for: Electron readout contrast enhancement in the parallel nuclear regime of an exchange-coupled donor spin qubit system
Data files
Mar 09, 2026 version files 561.47 MB
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Parallel_nuclei_data.zip
561.47 MB
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README.md
5.57 KB
Abstract
Recent experiments on donor spin qubits in silicon have leveraged the exchange interaction between electrons bound to separate donor nuclei to perform two-qubit operations. A consistently observed yet unexplained phenomenon in such systems is the significant increase in electron readout contrast, measured via Elzerman-style readout to a single-electron transistor (SET) island, when the donor nuclei are initialized in a parallel spin orientation compared to an anti-parallel orientation. In this work, we present a detailed analysis of the exchange-coupled donor system in the parallel nuclear regime and propose a physical mechanism for this effect. We attribute the enhanced readout contrast to an additional electron tunneling event to the SET during a single read period, when the donor nuclei are aligned in a parallel spin configuration.
Dataset DOI: 10.5061/dryad.1c59zw4b2
Description of the data and file structure
This is accompanying data for the journal article - Electron readout contrast enhancement in the parallel nuclear regime of an exchange-coupled donor spin qubit system.
Files and variables
File: Parallel_nuclei_data.zip
Description: This folder consists of 6 data folders and a Jupyter notebook .ipynb data analysis script.
Data folders:
The naming convention of the data folders is the following: #A_B_C where A is the number of the measurement for that date (i.e., #1 means this was the first measurement run that day for a given date), B is the name of the measurement that was run, and C is the time at which the measurement was run in the format HH-MM-SS.
Each data folder contains the following information:
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A subfolder entitled 'config'. This folder contains a series of .json files that contain key information regarding the measurement parameters that were in place when the data was taken. These .json files can be opened using any text editor or web browser. More specifically, there are four .json files within the config subfolder of each dataset:
1- 'analysis.json'. This file contains the parameters used for thresholding the single-shot readout electron readout data.
2 - 'connections.json’. This file specifies the instrument connections and voltage amplitude scale factors used when obtaining the dataset.
3- 'properties.json’. This file contains the key parameters of interest at the time at which the dataset was taken, for example: ESR frequencies, NMR frequencies, and measured lever arms between the control gates, donor atoms, and single-electron transistor.
4- 'pulses.json’. This file contains information regarding the pulses used to control the donor nuclei and electrons, including the pulse durations and frequencies.
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A subfolder entitled 'traces'. This folder contains the raw SET current traces from which the preprocessed data quantities have been derived. The raw traces are saved in HDF5 format, and can thus be accessed by a tool such as HDFview or the Python package h5py. When obtaining this data, the SET current is passed through a transimpedance amplifier, which converts the current into a voltage. The units of the y-axis of this data are therefore voltage. The x-axis of the data is samples, where the time difference between each sample = 1/sampling rate. Although the maximum sampling rate of the instrument was 200 megasamples per second, we used a sampling rate of 500 kilosamples per second, as this provided the required resolution for readout, without consuming too much memory. The time difference between each sample is therefore 1/500e3 = 2 us.
- A ‘snapshot.json’ file. This file contains a snapshot of all the information relevant to the experiment from which the data was obtained, including a copy of the contents of the ‘config’ folder, as well as a copy of all the instructions sent to the instruments during the measurement sequence. This .json file can be opened using any text editor or web browser.
- A series of .dat files containing the experimental results. To access this data, we strongly encourage readers to use the provided Jupyter notebook, which loads the data into a QCoDeS Data, and which can then be readily plotted. However, if required, the .dat files can also be opened with any text editor. These data files consist of all the parameters swept and measured in the experiment and follow a QCoDeS naming convention. The name of each .dat file contains the parameter that was measured in each dataset, e.g., a .dat file with ‘ESR.up_proportions’ in the name refers to a dataset consisting of the electron spin up proportions measured in this dataset. Similarly, a .dat file with ‘NMR.state_probabilities’ in the name consists of the nuclear state probabilities measured in the dataset. This data has been processed from the raw SET traces, and hence the units of the .dat files for ESR or NMR quantities are spin-up proportion.
- A .ipynb_checkpoints folder. This folder contains a snapshot of the current state of the 'Blip_analysis_code.ipynb' Jupyter notebook. This folder acts as a safety mechanism to save the state of the Jupyter notebook whenever a manual save of this notebook is carried out.
Data analysis code:
In the data folder is a Jupyter notebook with the filename 'Blip_analysis_code.ipynb'. This contains a series of cells that allow users to load the raw data and carry out the data analysis that was used to produce the publication figures.
Code/software
All measurements were performed and analyzed using the QCoDes data framework. The Jupyter notebook 'Blip_analysis_code.ipynb' contains the Python code used to analyze the data and produce the figures in the paper's main text and supplementary material, respectively.
These notebooks can be accessed and executed by carrying out the following steps:
- Download and install Anaconda (https://www.anaconda.com/download). This will install both Python and Jupyter Notebook.
- Install the QCoDesPython package. For more information on QCoDes and its installation, please see- https://microsoft.github.io/Qcodes/. Note that the version of QCoDes used for this analysis was 0.22.0.