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Manual Installation on Linux

The following instructions focus on Debian Linux but should work for Ubuntu as well. Note that if you can use Docker, all these steps are performed for you by Docker when the container is being created.

Dependencies

To run the code a few tools are needed:

  • The bazel build system. Install from bazel's homepage

  • We will need Python's pip tool to install packages and git to manage the source. Here is one way to install them:

    sudo apt-get install python3-pip
    sudo apt-get install git
  • We need Google's absl library, as well as numpy and scipy. Install with
   sudo python3 -m pip install absl-py
   sudo python3 -m pip install numpy
   sudo python3 -m pip install scipy
  • Finally, to get these source onto your computer:
    git clone https://github.com/qcc4cp/qcc.git

Build

Much of the code is in Python and will run out of the box. There is some C++ for the high performance simulation which requires configuration.

The file src/lib/BUILD contains the build rule for the C++ xgates extension module. This module needs to be able to access the Python header (Python.h), as well as certain numpy headers. These files' location may be different on your build machine. The location is controlled with the numpy and python dependencies, which we explain in a second:

cc_library(
    name = "xgates",
    srcs = [
        "xgates.cc",
    ],
    copts = [
        "-O3",
        "-ffast-math",
    	"-march=skylake",
        "-DNPY_NO_DEPRECATED_API",
        "-DNPY_1_7_API_VERSION",
    ],
    deps = [
        "@third_party_numpy//:numpy",
        "@third_party_python//:python",
    ],
)

There is a subtlety about bazel: All headers must be within the source tree, or in /usr/include/... To work around this, we have to point bazel to the installation directories of numpy and python. The specification for the external installations is in the WORKSPACE file. Point path to your installation's header files, excluding the final include part of the path. The include path is specified in the files external/numpy.BUILD and external/python.BUILD.

new_local_repository(
    name = "third_party_numpy",
    build_file = __workspace_dir__ + "/numpy.BUILD",
    # Configure:
    path = "/usr/local/lib/python3.7/dist-packages/numpy/core/",
)

new_local_repository(
    name = "third_party_python",
    build_file = __workspace_dir__ + "/python.BUILD",
    # Configure:
    path = "/usr/include/python3.9",
)

Once xgates builds successfully, it is imported into circuit.py. At the top of this file is the import statement that might need to be adjusted:

# Configure: This line might have to change, depending on
#            the current build environment.
#
# Google internal:
# import xgates
#
# GitHub Linux:
import libxgates as xgates

Additionally, to enable Python to find the extension module, make sure to include in PYTHONPATH the directory where the generated xgates.so or libxgates.so is being generated. For example:

export PYTHONPATH=$PYTHONPATH:/home/usrname/qcc/bazel-bin/src/lib

bazel also attempts to use the Python 2 interpreter python. If it is not available on a system, install via:

sudo apt-get install python

Run

To build the library and test for correct installation, go to src/lib and run:

    bazel build all
    bazel test ...

    # Make sure to set PYTHONPATH (once):
    export PYTHONPATH=$PYTHONPATH:/home/usrname/qcc/bazel-bin/src/lib

    # Ensure xgates was built properly:
    bazel run circuit_test

Refer to the main page to learn how to run the individual algorithms. Typically, in the qcc/src directory, you would run something like:

for algo in `ls -1 *py | sed s@.py@@g`
do
   bazel run $algo
done