DanielCohenHillel / pyEPR

Powerful, automated analysis and design of quantum microwave chips & devices [Energy-Participation Ratio and more]

Welcome to pyEPR 🍻!

Automated Python module for the design and quantization of Josephson quantum circuits

Abstract: Superconducting circuits incorporating non-linear devices, such as Josephson junctions and nanowires, are among the leading platforms for emerging quantum technologies. Promising applications require designing and optimizing circuits with ever-increasing complexity and controlling their dissipative and Hamiltonian parameters to several significant digits. Therefore, there is a growing need for a systematic, simple, and robust approach for precise circuit design, extensible to increased complexity. The energy-participation ratio (EPR) approach presents such an approach to unify the design of dissipation and Hamiltonians around a single concept — the energy participation, a number between zero and one — in a single-step electromagnetic simulation. This markedly reduces the required number of simulations and allows for robust extension to complex systems. The approach is general purpose, derived ab initio, and valid for arbitrary non-linear devices and circuit architectures. Experimental results on a variety of circuit quantum electrodynamics (cQED) devices and architectures, 3D and flip-chip (2.5D), have been demonstrated to exhibit ten percent to percent-level agreement for non-linear coupling and modal Hamiltonian parameters over five-orders of magnitude and across a dozen samples. Here, in this package, all routines of the EPR approach are fully automated.

Documentation

Read the docs here.

Legacy users

Warning: pyEPR organization was significnatly improved in v0.8-dev (starting 2020; current branch: master [to be made stable soon]). If you used a previous version, you will find that all key classes have been renamed. Please, see the tutorials and docs. In the meantime, if you cannot switch yet, revert to use the stable v0.7.

Who uses pyEPR?

• Yale University, Michel Devoret lab QLab, CT, USA
• Yale University, Rob Schoelkopf lab RSL, CT, USA
• IBM Quantum
• QUANTIC (QUANTUM INFORMATION CIRCUITS), PARISINRIA, ENS, MINES PARISTECH, UPMC, CNRS. Groups of Zaki Leghtas and team. France
• Quantum Circuit Group Emanuel FLurin, Benjamin Huard, Ecole Normale Supérieure de Lyon, France
• UC Berkeley, Quantum Nanoelectronics Laboratory, Irfan Siddiqi, CA, USA
• Quantum Circuits, Inc., CT, USA
• Serge Rosenblum Lab in the Weizmann Instatue, Israel
• Peter Leek Lab, UK
• Britton Plourde Lab, Syracuse University, USA
• Javad Shabani Lab Quantum Materials & Devices, NYU, NY, USA
• ... and many more! (Please e-mail [e-mail] with updates.)

How do I cite pyEPR when I publish?

Cite the following and/or e-mail [e-mail] or zaki leghtas

• Z.K. Minev, Ph.D. Dissertation, Yale University (2018), Chapter 4. arXiv:1902.10355
• Z.K. Minev, Z. Leghtas, et al. (to appear soon on arXiv) (2020)

Contents:

• Start here: Using pyEPR
• Video Tutorials
• Setup and Installation
• HFSS Project Setup for pyEPR
• Troubleshooting pyEPR
• Authors and Contributors

Start here: Using pyEPR

1. Fork 🍴 the pyEPR top-level repository on GitHub. (How to fork a GitHub repo?). Share some love by staring ⭐ pyEPR.
2. Clone 👇 your forked repository locally. (How to clone a GitHub repo?). Setup the pyEPR python code by following Installation and Python Setup.
3. Tutorials Learn how to use using the jupyter notebook tutorials
4. Stay up to date Enjoy and make sure to git add the master remote branch git remote add MASTER_MINEV git://github.com/zlatko-minev/pyEPR.git (help?).
5. Cite pyEPR arXiv:1902.10355 and enjoy! 🎂

Start-up example

Jupyter notebook tutorials

The following code illustrates how to perform a complete analysis of a simple two-qubit, one-cavity device in just a few lines of code with pyEPR. In the HFSS file, before running the script, first specify the non-linear junction rectangles and variables (see Sec. pyEPR Project Setup in HFSS). All operations in the eigen analysis and Hamiltonian computation are fully automated. The results are saved, printed, and succinctly plotted.

# Load pyEPR. See the tutorial notebooks!
import pyEPR as epr

# 1. Connect to your Ansys, and load your design
pinfo = epr.ProjectInfo(project_path = r'C:\sim_folder',
                        project_name = r'cavity_with_two_qubits',
                        design_name  = r'Alice_Bob')


# 2a. Non-linear (Josephson) junctions
pinfo.junctions['jAlice'] = {'Lj_variable':'Lj_alice', 'rect':'rect_alice', 'line': 'line_alice', 'Cj_variable':'Cj_alice'}
pinfo.junctions['jBob']   = {'Lj_variable':'Lj_bob',   'rect':'rect_bob',   'line': 'line_bob', 'Cj_variable':'Cj_bob'}
pinfo.validate_junction_info() # Check that valid names of variables and objects have been supplied.

# 2b. Dissipative elements: specify
pinfo.dissipative.dielectrics_bulk    = ['si_substrate', 'dielectic_object2'] # supply names of hfss objects
pinfo.dissipative.dielectric_surfaces = ['interface1', 'interface2']

# 3.  Perform microwave analysis on eigenmode solutions
eprd = epr.DistributedAnalysis(pinfo)
if 1: # automatic reports
  eprd.quick_plot_frequencies(swp_var) # plot the solved frequencies before the analysis
  eprd.hfss_report_full_convergence() # report convergen
eprd.do_EPR_analysis()

# 4a.  Perform Hamiltonian spectrum post-analysis, building on mw solutions using EPR
epra = epr.QuantumAnalysis(eprd.data_filename)
epra.analyze_all_variations(cos_trunc = 8, fock_trunc = 7)

# 4b. Report solved results
swp_variable = 'Lj_alice' # suppose we swept an optimetric analysis vs. inductance Lj_alice
epra.plot_hamiltonian_results(swp_variable=swp_variable)
epra.report_results(swp_variable=swp_variable, numeric=True)
epra.quick_plot_mode(0,0,1,numeric=True, swp_variable=swp_variable)

pyEPR Video Tutorials

Tutorial 1 - Overview	Tutorial 2 - Setup of Conda & Git	Tutorial 3 - Setup of Packages & Config

Jupyter notebook tutorials

Installation and setup of pyEPR

---

Use pyEPR directly from the source, and pull updates from the master git repo, since we often update it. The following steps explain how to set up Python 3, fork the pyEPR repo and use it. Please keep up to date with pyEPR by using git. We like to make it simple using a git-gui manager, SourceTree or GitHub Desktop.

Recommended procedure.

1. Install Python 3.x, we recommend the Anaconda distribution.
   The code is currently under dev with Python 3.6/7. It was developed under 2.7 and should still be compatible.
   After the install, make sure you configure your system PATH variables. On Windows, in the taskbar search or control panel, search for "Edit environment variables for your account". In the section System Variables, find the PATH environment variable and select it. Click Edit. PlaceC:\Anaconda3;C:\Anaconda3\Scripts;C:\Anaconda3\Library\bin; at the beginning of the path. If you have a previous Python installation this step is very important, especially to compile the qutip module. You may verity your path using the following command in the Command Prompt (terminal): sh $ echo %PATH%

2. Install the required packages, including pint, qutip, and attrdict. In a terminal window

conda install -c conda-forge pint
conda install -c conda-forge qutip
pip install attrdict

3. Fork this pyEPR repository on GitHub with your GitHub account. You may clone the fork to your PC and manage it using the SourceTree git-gui manager.
4. Add the pyEPR repository folder to your python search path. Make sure to add the git remote to the master is set up, git remote add MASTER_MINEV git://github.com/zlatko-minev/pyEPR.git! (Help?)
5. Edit pyEPR module _config_user.py to set your data-saving directory and other parameters of interest. (To keep your changes local, use the shell command git update-index --skip-worktree _config_user.py in the pyEPR/pyEPR folder)
6. **ENJOY and cite pyEPR! ** 👍

Note for Mac/Linux.

Follow the same instructions above. You shouldn't have to install mingw or modify distutils.cfg, since your distribution should come with gcc as the default compiler.

HFSS Project Setup for pyEPR

---

Eigenmode Design --- How to set up junctions

You may find an advised work flow and some setup tips here.

1. Define circuit geometry & electromagnetic boundary condition (BC).
2. Junction rectangles and BC: Create a rectangle for each Josephson junction and give it a good name; e.g., jAlice for a qubit named Alice. We recommend 50 x 100 um rectangle for a simple simulation, although orders of magnitude smaller rectangles work as well. Note the length of this junction, you will supply it to pyEPR. Assign a Lumped RLC BC on this rectangle surface, with an inductance value given by a local variable, Lj1 for instance. The name of this variable will also be supplied to the pyEPR.
3. Over each junction rectangle draw a model polyline to define give a sense of the junction current-flow direction. This line should spans the length of the full junction rectangle. Define it using an object coordinate system on the junction rectangle (so that they move together when the geometry is altered). The name of this line will be supplied to the pyEPR module.
4. Meshing.
5. Lightly mesh the thin-film metal BC. Lightly mesh the junction rectangles.
6. Simulation setup
7. We recommend mixed order solutions.

Troubleshooting pyEPR

---

First run: pint error: system='mks' unknown.

Please update to pint version newer than 0.7.2. You may use

pip install pint --upgrade

No attribute StringIO during do_EPR_analysis()

AttributeError: module 'pandas.compat' has no attribute 'StringIO'

Caleb pointed this out, see here and here for solution. You need to change the pandas version. [pyEPR to be upgraded]

When importing qutip an error occurs AttributeError: module 'numpy' has no attribute '__config__'

You probably have to update your numpy installation. For me, the following bash sequence worked:

conda update qutip
conda update numpy

Qutip installation

You may also choose to install the optional qutip package for some advanced numerical analysis of the Hamiltonian. We use Qutip to handle quantum objects. Follow the instruction on their website. As of Aug. 2017, qutip is part of conda, and you can use

conda install qutip

If this doesn't work, try installing from conda forge

conda install -c conda-forge qutip

Qutip installation -- alternative, manual install

If you wish to install manually, follow the following procedure. Some of this can get a bit tricky at times. First, you need to install a C compiler, since qutip uses Cython. If you dont have VS9, gcc, or mingw installed, the following works:

pip install -i https://pypi.anaconda.org/carlkl/simple mingwpy

Let anaconda know to use this compiler by creating the file C:\Anaconda2\Lib\distutils\distutils.cfg with the following content

[build]
compiler = mingw32
[build_ext]
compiler = mingw32

Next, let's install qutip. You can choose to use conda intall or pip install, or pull from the git directly as done here:

conda install git
pip install git+https://github.com/qutip/qutip.git

COM Error on opening HFSS

Check the project and design file names carefully. Make sure that the file-path doesn't have apostrophes or other bad characters, such as in C:\Minev's PC\my:Project. Check that HFSS hasn't popped up an error dialogue, such as "File locked." Manually open HFSS and the file.

COM error on calculation of expression

Either HFSS popped an error dialog, froze up, or you miss-typed the name of something.

HFSS refuses to close

If your script terminates improperly, this can happen. pyHFSS tries to catch termination events and handle them. Your safety should be guaranteed however, if you call hfss.release() when you have finished. Use the Task-manager (Activity Monitor on MAC) to kill HFSS if you want.

Parametric Sweep Error

When running a parametric sweep in HFSS, make sure you are actually saving the fields for each variation before running pyEPR. This can be done by right-clicking on your ParametricSetup -> properties -> options -> "Save Fields and Mesh".

Spyder pops up command window cmd with tput.exe executed

This problem is due to pandas 0.20.1, update to 0.20.3 or better solves this issue.

ValueError: cannot set WRITEABLE flag to True of this array

This error happens when trying to read in an hdf file with numpy version 1.16, see git issue here. A solution is to downgrade numpy to 1.15.4 or upgrade to newer versions of hdf and numpy.

Authors and Contributors

• Authors: Zlatko Minev & Zaki Leghtas, with contributions from many friends and colleagues.
• 2015 - present.
• Contributors: Phil Rheinhold, Lysander Christakis, Devin Cody, ... Original versions of pyHFSS.py and pyNumericalDiagonalization.py contributed by Phil Rheinhold, excellent original repo.
• Terms of use: Use freely and kindly cite the paper (arXiv link to be posted here) and/or this package.
• How can I contribute? Contact Z. Minev or Z. Leghtas.