Detecting and Tracking Drift in Quantum Information Processors

<p>This is supplemental data and code for:<strong> </strong>T. Proctor et al, <a href="https://www.nature.com/articles/s41467-020-19074-4"><em>Detecting and tracking drift in quantum information processors</em></a>,&nbsp;Nat. Comm. 11, 5396 (2020).</p> <p>Please direct any questions to Timothy Proctor&nbsp;(tjproct@sandia.gov).</p> <p>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&nbsp;<a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>The analysis was run using pyGSTi commit 7c6ddd1de209b795ea39bfb69d010b687e812d07. This code does&nbsp;<em>not</em>&nbsp;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).</p> <p>Below is a basic guide to navigating this SI:</p> <p><strong>Time-resolved Ramsey tomography on experimental data.</strong></p> <p><em>Directory: ramsey/experiment</em></p> <p>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.</p> <p><strong>Time-resolved randomized benchmarking (RB) on simulated data.</strong></p> <p><em>Directory: rb/simulation</em></p> <p>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.</p> <p><strong>Time-resolved gate set tomography (GST) on simulated data.</strong></p> <p><em>Directory: gst/simulation</em></p> <p>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 &quot;data&quot; folder. All the code is contained in the &quot;analysis&quot; folder. This contains the following code files:</p> <ul> <li>create_simulated_data.py : this generates the simulated data.&nbsp;This was run using MPI on 20 cores.</li> <li>drift.ipynb : this contains the general circuit-agnostic drift analysis.</li> <li>trgst_fit.py : this contains the TR-GST model-fitting code.&nbsp;This was run using MPI on 20 cores.</li> <li>tdmodel.py : encodes the general time-dependent model that the data is fit to.</li> </ul> <p><strong>Time-resolved gate set tomography (GST) on experimental data.</strong></p> <p><em>Directory: gst/experiments</em></p> <p>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 &quot;data/1&quot; and &quot;data/2&quot;, corresponding to the first and second experiment, respectively. All analysis code is contained in the &quot;analysis&quot; folder. This contains the following code files:</p> <ul> <li>drift.ipynb : this contains the general circuit-agnostic drift analysis.</li> <li>gst.ipynb : this contains the standard GST analysis, used to inform the TR-GST analysis.</li> <li>trgst_fit.py : this contains the TR-GST model-fitting code.&nbsp;This was run using MPI on 20 cores.</li> <li>trgst_plotting.ipnyb : this contains code that analyzes the results of the TR-GST fit.</li> <li>tdmodel.py : encodes the general time-dependent model that the data is fit to.</li> </ul>