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

.. only:: html

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

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

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

.. _sphx_glr_gallery_qft_with_tn.py:


Large-scale simulations with tensor network simulator
=====================================================

In this example, we demonstrate simulation of MBQC involving 10k+ nodes.

You can also run this code on your browser with `mybinder.org <https://mybinder.org/>`_ - click the badge below.

.. image:: https://mybinder.org/badge_logo.svg
 :target: https://mybinder.org/v2/gh/TeamGraphix/graphix-examples/HEAD?labpath=qft_with_tn.ipynb

Firstly, let us import relevant modules and define the circuit:

.. GENERATED FROM PYTHON SOURCE LINES 16-47

.. code-block:: default

    import numpy as np

    from graphix import Circuit


    def cp(circuit, theta, control, target):
        circuit.rz(control, theta / 2)
        circuit.rz(target, theta / 2)
        circuit.cnot(control, target)
        circuit.rz(target, -1 * theta / 2)
        circuit.cnot(control, target)


    def qft_rotations(circuit, n):
        circuit.h(n)
        for qubit in range(n + 1, circuit.width):
            cp(circuit, np.pi / 2 ** (qubit - n), qubit, n)


    def swap_registers(circuit, n):
        for qubit in range(n // 2):
            circuit.swap(qubit, n - qubit - 1)
        return circuit


    def qft(circuit, n):
        for i in range(n):
            qft_rotations(circuit, i)
        swap_registers(circuit, n)









.. GENERATED FROM PYTHON SOURCE LINES 48-49

We will simulate 55-qubit QFT, which requires graph states with more than 10000 nodes.

.. GENERATED FROM PYTHON SOURCE LINES 49-66

.. code-block:: default


    n = 55
    print(f"{n}-qubit QFT")
    circuit = Circuit(n)

    for i in range(n):
        circuit.h(i)
    qft(circuit, n)

    # standardize pattern
    pattern = circuit.transpile(opt=True).pattern
    pattern.standardize()
    pattern.shift_signals()
    nodes, edges = pattern.get_graph()
    print(f"Number of nodes: {len(nodes)}")
    print(f"Number of edges: {len(edges)}")





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

 .. code-block:: none

    55-qubit QFT
    Number of nodes: 10560
    Number of edges: 13475




.. GENERATED FROM PYTHON SOURCE LINES 67-69

Using efficient graph state simulator `graphix.GraphSim`, we can classically preprocess Pauli measurements.
We are currently improving the speed of this process by using rust-based graph manipulation backend.

.. GENERATED FROM PYTHON SOURCE LINES 69-72

.. code-block:: default

    pattern.perform_pauli_measurements(use_rustworkx=True)









.. GENERATED FROM PYTHON SOURCE LINES 73-75

To specify TN backend of the simulation, simply provide as a keyword argument.
here we do a very basic check that one of the statevector amplitudes is what it is expected to be:

.. GENERATED FROM PYTHON SOURCE LINES 75-86

.. code-block:: default


    import time

    t1 = time.time()
    tn = pattern.simulate_pattern(backend="tensornetwork")
    value = tn.get_basis_amplitude(0)
    t2 = time.time()
    print("amplitude of |00...0> is ", value)
    print("1/2^n (true answer) is", 1 / 2**n)
    print("approximate execution time in seconds: ", t2 - t1)





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

 .. code-block:: none

    amplitude of |00...0> is  2.775557561562938e-17
    1/2^n (true answer) is 2.7755575615628914e-17
    approximate execution time in seconds:  25.010623931884766





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

   **Total running time of the script:** ( 0 minutes  59.125 seconds)


.. _sphx_glr_download_gallery_qft_with_tn.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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