quimb.experimental.cluster_update.cluster_update_old
====================================================

.. py:module:: quimb.experimental.cluster_update.cluster_update_old

.. autoapi-nested-parse::

   Implementation of arbitrary geometry wavefunction cluster update.



Attributes
----------

.. autoapisummary::

   quimb.experimental.cluster_update.cluster_update_old.gate_inds_nn_fit_


Classes
-------

.. autoapisummary::

   quimb.experimental.cluster_update.cluster_update_old.ClusterUpdateNNGen


Functions
---------

.. autoapisummary::

   quimb.experimental.cluster_update.cluster_update_old.gate_inds_nn_fit


Module Contents
---------------

.. py:function:: gate_inds_nn_fit(self, G, ind1, ind2, max_bond=None, method='als', pregauge=2, init_simple_guess=True, steps=10, fit_opts=None, contract_opts=None, inplace=False)

   Gate two nearest neighbor outer indices, using full fitting of
   reduced tensors with respect to the environment. This is more accurate
   than a simple reduced gate when restricting the bond dimension.

   :param G: The gate to fit.
   :type G: array_like
   :param ind1: The indices to gate.
   :type ind1: str
   :param ind2: The indices to gate.
   :type ind2: str
   :param max_bond: The maximum bond dimension to use. If ``None``, use the maximum
                    bond dimension that the tensors currently share.
   :type max_bond: int, optional
   :param method: The method to use for fitting.
   :type method: {'als', 'autodiff'}, optional
   :param pregauge: How many times to locally canonize from the purified environment
                    tensor to both the left and right reduced tensors.
   :type pregauge: int, optional
   :param init_simple_guess: Whether to use a 'simple update' guess for the initial guess. This
                             can be quite high quality already if pregauging is used.
   :type init_simple_guess: bool, optional
   :param steps: The number of steps to use for fitting, can be ``0`` in which case
                 the initial guess is used, which in conjuction with the
                 envinronment pregauging can still be quite high quality.
   :type steps: int, optional
   :param inplace: Whether to update the tensor network in place.
   :type inplace: bool, optional
   :param contract_opts: Supplied to
                         :meth:`~quimb.tensor.tensor_core.TensorNetwork.contract`.


.. py:data:: gate_inds_nn_fit_

.. py:class:: ClusterUpdateNNGen(psi0, ham, tau=0.01, D=None, cluster_radius=1, cluster_fillin=0, gauge_smudge=1e-06, imag=True, gate_opts=None, ordering=None, second_order_reflect=False, compute_energy_every=None, compute_energy_final=True, compute_energy_opts=None, compute_energy_fn=None, compute_energy_per_site=False, callback=None, keep_best=False, progbar=True)

   Bases: :py:obj:`quimb.tensor.tnag.tebd.SimpleUpdateGen`


   Cluster update for arbitrary geometry nearest neighbor hamiltonians.
   This keeps track of simple update style gauges, in order to approximately
   partial trace beyond ``cluster_radius`` and form an approximate environment
   for two nearest neighbor sites that be used to fit the gate with higher
   quality than simple update only.


   .. py:attribute:: cluster_radius
      :value: 1



   .. py:attribute:: cluster_fillin
      :value: 0



   .. py:attribute:: gauge_smudge
      :value: 1e-06



   .. py:method:: gate(U, where)

      Application of a single gate ``G`` at ``where``.