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This Computer Software is developed under Agreement HR00112230006 between Rigetti & Co, LLC and the Defense Advanced Research Projects Agency (DARPA). Use, duplication, or disclosure is subject to the restrictions as stated in Agreement HR00112230006 between the Government and the Performer. This Computer Software is provided to the U.S. Government with Unlimited Rights; refer to LICENSE file for Data Rights Statements. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the DARPA.
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The Rigetti Resource Estimation (RRE) is a resource estimation tool for future fault-tolerant superconducting quantum hardware based on logical, physical-level inputs and graph state compilation to measurement-based quantum computing and surface code operations (see PhysRevA 73 022334, Quantum Sci. Technol. 2 025003, and arXiv:2209.07345). The program is written in Python and supports a variety of estimation methods.
RRE can be installed and used as a stand-alone tool from the source.
You will need to have Poetry installed on your system to install the RRE tool from the source. The current official installer can be invoked for Linux using the following command:
curl -sSL https://install.python-poetry.org | python3 -
RRE uses Jabalizer as its default fault-tolerant compiler for all
graph-state-based resource estimates. Jabalizer will be installed (through Julia's official packages only on the first
run) and invoked upon each run of RRE when the user selects the jabalizer estimation method. We provide a
jabalizer_wrapper.jl script that automatically registers
dependent Julia libraries, configures, and runs Jabalizer.
You must have Julia installed on your system to run Jabalizer. We recommend installing Julia using Juliaup; for Linux installations execute the following command:
curl -fsSL https://install.julialang.org | sh -s -- -y
You will need to download the repository source using the GitHub download feature or cloning the repository
using Git. Once you have done this, you will have a local copy of the source under rigetti_resource_estimation.
Install the Python package and its dependencies using Poetry by executing the following command:
poetry install
Our front-end resource estimation pipeline is in
estimation_pipline.py and can either be run as a command-line
tool or directly as a library dependency via the estimation_pipeline() function. Try to run our command-line tool
using the following command, which will display help on all options:
poetry run python src/rigetti_resource_estimation/estimation_pipeline.py --help
Our tool includes a default four-qubit Quantum Fourier Transform OpenQASM 2.0 example circuit that you may try and edit as desired. To run this example with the default Jabalizer graph-state compiler and log the results to the console, run the following command:
poetry run python src/rigetti_resource_estimation/estimation_pipeline.py --log="INFO"
A complete example would be to estimate resources for a circuit of your choice by specifying the QASM input file, with the results appended to a CSV file and the intermediate adjacency list of the compiled graph state also saved for characteristic analysis. Try the following command as an example and tailor it to your needs:
poetry run python src/rigetti_resource_estimation/estimation_pipeline.py --log "DEBUG" \
--circ-path examples/input/qft4.qasm --est-method "jabalizer" --output examples/test.csv \
--graph-state-opt "save"
A possible use of our tool is as a design aid, sweeping specified fields from the params.yaml configuration file over
ranges of values to understand how hardware design changes might impact final resource requirements. An example of this
where we inspect the impact of varying the time needed for an inter-module surface-code "tock" is:
poetry run sweep ftqc.intermodule_tock_sec "2e-6, 3e-6, 4e-6, 6e-6" \
--circ-path examples/input/qft4.qasm --output-csv examples/test.csv
We also have a Jupyter demo notebook with more examples, demo.ipynb, which you may find helpful as a starting point for your analysis. You can launch Jupyter Lab using the following command and then navigate to the "examples" directory to load this notebook:
make jupyter-lab
You can control typical development and testing tasks by the Makefile; you can view the list of options by running:
make
Formatting and style checks are run in CI, as are the package and demonstration notebook tests. We suggest you lint your code before pushing by running:
make lint
All tests can be invoked with coverage enabled by running the command:
make test-package
Check that you are invoking the command-line tool from the repository root and that all paths you have provided are correct relative to the current working directory.
2. I get errors like You are using both a virtual and conda environment, cannot figure out which to use!.
You have both the conda and virtualenv Python environments activated, and juliapkg cannot decide which one to use.
One solution is to run conda deactivate several times, ensuring all Conda envs and vars are unset, and then switch
to a desired env for poetry either through the poetry run commands or in an interactive shell via poetry shell.
You could always use poetry env use /full/path/to/python to change the environment Poetry uses -- see
here for more details.
This is an issue that occurs if you use pyenv to manage your python installs. PythonCall is trying to access the
shared libraries from your python install. This is a
known problem.. The fix is to reinstall python, via pyenv,
only by using the command env PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install <python version>. In this way,
pyenv makes the shared libraries available to PythonCall. More information on this can be found
here.