DOI: 10.5281/zenodo.4033076
title: Detecting and Tracking Drift in Quantum Information Processors

description: This is supplemental data and code for:<strong> </strong>T. Proctor et al, <em>Detecting and tracking drift in quantum information processors</em>, Nat. Comm. 11, 5396 (2020). Please direct any questions to Timothy Proctor (tjproct@sandia.gov). This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at https://github.com/pyGSTio/pyGSTi. The analysis was run using pyGSTi commit 7c6ddd1de209b795ea39bfb69d010b687e812d07. This code does <em>not</em> work on the latest full release of pyGSTi (0.9.9). It is anticipated that it will work with the next full release of pyGSTi (0.9.10). Below is a basic guide to navigating this SI: <strong>Time-resolved Ramsey tomography on experimental data.</strong> <em>Directory: ramsey/experiment</em> This folder contains the data and analysis code for the time-resolved Ramsey experiment, the results of which are presented in Figure 1 of the paper. The folder contains a single Jupyter notebook, which runs all of the data analysis. <strong>Time-resolved randomized benchmarking (RB) on simulated data.</strong> <em>Directory: rb/simulation</em> This folder contains the data and analysis code for the simulation of time-resolved RB, the results of which are presented in Figure 2 of the paper. The folder contains a single Jupyter notebook, which runs all of the data analysis on the simulated data, and which can be used to run new simulations with the same noise model. <strong>Time-resolved gate set tomography (GST) on simulated data.</strong> <em>Directory: gst/simulation</em> This contains the data and analysis code for the simulation of time-resolved GST, the results of which are presented in Figure 2 of the paper. The raw simulated data is contained in the "data" folder. All the code is contained in the "analysis" folder. This contains the following code files: create_simulated_data.py : this generates the simulated data. This was run using MPI on 20 cores. drift.ipynb : this contains the general circuit-agnostic drift analysis. trgst_fit.py : this contains the TR-GST model-fitting code. This was run using MPI on 20 cores. tdmodel.py : encodes the general time-dependent model that the data is fit to. <strong>Time-resolved gate set tomography (GST) on experimental data.</strong> <em>Directory: gst/experiments</em> This folder contains the data and analysis code for the two time-resolved GST experiments, the results of which are presented in Figure 3 of the paper. The raw data is contained in the two folders "data/1" and "data/2", corresponding to the first and second experiment, respectively. All analysis code is contained in the "analysis" folder. This contains the following code files: drift.ipynb : this contains the general circuit-agnostic drift analysis. gst.ipynb : this contains the standard GST analysis, used to inform the TR-GST analysis. trgst_fit.py : this contains the TR-GST model-fitting code. This was run using MPI on 20 cores. trgst_plotting.ipnyb : this contains code that analyzes the results of the TR-GST fit. tdmodel.py : encodes the general time-dependent model that the data is fit to.
description: SAND2020-9643 O