.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/fusion_extraction.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_gallery_fusion_extraction.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_fusion_extraction.py:


Designing fusion network to generate resource graph state
==========================================================

In this example, we decompose a graph state into a set of GHZ and linear cluster resource states,
such that fusion operations can be used on these 'micro-resource' states to obtain the desired graph state.
This is an important compilation stage to perform MBQC on discrete-variable optical QPUs.

The decomposition algorithm is based on [1].

[1] Zilk et al., A compiler for universal photonic quantum computers,
2022 `arXiv:2210.09251 <https://arxiv.org/abs/2210.09251>`_

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.. code-block:: default

    import itertools

    import graphix
    from graphix.extraction import get_fusion_network_from_graph





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/graphix/envs/127-support-python312/lib/python3.10/site-packages/cotengra/hyperoptimizers/hyper.py:33: UserWarning: Couldn't import `kahypar` - skipping from default hyper optimizer and using basic `labels` method instead.
      warnings.warn(




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Here we say we want a graph state with 9 nodes and 12 edges.
We can obtain resource graph for a measurement pattern by using :code:`nodes, edges = pattern.get_graph()`.

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.. code-block:: default

    gs = graphix.GraphState()
    nodes = [0, 1, 2, 3, 4, 5, 6, 7, 8]
    edges = [(0, 1), (1, 2), (2, 3), (3, 0), (3, 4), (0, 5), (4, 5), (5, 6), (6, 7), (7, 0), (7, 8), (8, 1)]
    gs.add_nodes_from(nodes)
    gs.add_edges_from(edges)
    gs.draw()




.. image-sg:: /gallery/images/sphx_glr_fusion_extraction_001.png
   :alt: fusion extraction
   :srcset: /gallery/images/sphx_glr_fusion_extraction_001.png
   :class: sphx-glr-single-img





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Decomposition with GHZ and linear cluster resource states with no limitation in their sizes.

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.. code-block:: default

    get_fusion_network_from_graph(gs)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    [GHZ[0, 7, 5, 3, 1], GHZ[1, 2, 8], LINEAR[2, 3, 4, 5, 6, 7, 8]]



.. GENERATED FROM PYTHON SOURCE LINES 37-43

If you want to know what nodes are fused in each resource states,
you can use :func:`~graphix.extraction.get_fusion_nodes` function.
Currently, we consider only type-I fusion. See [2] for the definition of fusion.

[2] Daniel E. Browne and Terry Rudolph. Resource-efficient linear optical quantum computation.
Physical Review Letters, 95(1):010501, 2005.

.. GENERATED FROM PYTHON SOURCE LINES 43-50

.. code-block:: default

    fused_graphs = get_fusion_network_from_graph(gs)
    for idx1, idx2 in itertools.combinations(range(len(fused_graphs)), 2):
        print(
            f"fusion nodes between resource state {idx1} and "
            f"resource state {idx2}: {graphix.extraction.get_fusion_nodes(fused_graphs[idx1], fused_graphs[idx2])}"
        )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    fusion nodes between resource state 0 and resource state 1: [1]
    fusion nodes between resource state 0 and resource state 2: [7, 5, 3]
    fusion nodes between resource state 1 and resource state 2: [2, 8]




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You can also specify the maximum size of GHZ clusters and linear clusters available,
for more realistic fusion scheduling.

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.. code-block:: default

    get_fusion_network_from_graph(gs, max_ghz=4, max_lin=4)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    [GHZ[0, 7, 5, 3], GHZ[1, 8, 2, 0], LINEAR[2, 3, 4, 5], LINEAR[5, 6, 7, 8]]




.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  15.514 seconds)


.. _sphx_glr_download_gallery_fusion_extraction.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: fusion_extraction.py <fusion_extraction.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: fusion_extraction.ipynb <fusion_extraction.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_