.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/BoseHubbard_1d_groundstate.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_BoseHubbard_1d_groundstate.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_BoseHubbard_1d_groundstate.py:


Ground state simulation of the Bose-Hubbard model.
==================================================

.. GENERATED FROM PYTHON SOURCE LINES 16-98

.. code-block:: Python


    # pylint: disable=invalid-name

    import numpy as np

    import qtealeaves as qtl
    from qtealeaves.models import get_bose_hubbard_1d

    # Reference ground state energies from OSMPS
    # Keys are ... L, J, symmetry_sector
    ref_osmps = {
        (8, 0.5, None): -4.7358608335175,
        (8, 0.5, 8): -4.66662145271015,
        (8, 0.5, 7): -4.4656708344888,
    }


    def main(tn_type=5, statics_method=2, output_folder=None):
        """
        Main method for the ground state calculation fo the
        1d Bose-Hubbard model.

        **Arguments**

        tn_type : int, optional
            Choose 5 for python-TTN, 6 for python-MPS.
            Default to 5.

        statics_method : integer, optional
            Method to run ground state search for this/all iteration.
            0 : default (2)
            1 : sweep
            2 : sweep with space expansion (can still be reduced to sweep
                during the simulation based on a energy condition)

        output_folder : str | None, optional
            Output folder. Default to None.
        """
        if output_folder is None:
            output_folder = lambda params: "BOHU_L%d" % (params["L"])

        model, my_ops = get_bose_hubbard_1d()

        my_conv = qtl.convergence_parameters.TNConvergenceParameters(
            max_iter=7, max_bond_dimension=16, statics_method=statics_method
        )
        my_obs = qtl.observables.TNObservables()

        simulation = qtl.QuantumGreenTeaSimulation(
            model,
            my_ops,
            my_conv,
            my_obs,
            tn_type=tn_type,
            folder_name_output=output_folder,
            has_log_file=False,
            store_checkpoints=False,
        )

        params = []
        params.append({"L": 8, "U": 1.0, "J": 0.5})

        simulation.run(params, delete_existing_folder=True)

        for elem in params:
            e0_tn = simulation.get_static_obs(elem)["energy"]
            e0_osmps = ref_osmps[(elem["L"], elem["J"], None)]

            print("Energies TTN vs OSMPS", e0_tn, e0_osmps)

            assert np.abs(e0_tn - e0_osmps) < 1e-4

        print(
            f"\nExample `{__file__}` ran successfully; "
            + "ground state energy at least correct up to 1e-4."
        )

        return


    if __name__ == "__main__":
        main()


.. _sphx_glr_download_auto_examples_BoseHubbard_1d_groundstate.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: BoseHubbard_1d_groundstate.ipynb <BoseHubbard_1d_groundstate.ipynb>`

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

      :download:`Download Python source code: BoseHubbard_1d_groundstate.py <BoseHubbard_1d_groundstate.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: BoseHubbard_1d_groundstate.zip <BoseHubbard_1d_groundstate.zip>`


.. only:: html

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

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