quimb.tensor.tensor_mera
========================

.. py:module:: quimb.tensor.tensor_mera

.. autoapi-nested-parse::

   Tools for building and computing with MERA.



Classes
-------

.. autoapisummary::

   quimb.tensor.tensor_mera.MERA


Functions
---------

.. autoapisummary::

   quimb.tensor.tensor_mera.is_power_of_2


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

.. py:function:: is_power_of_2(x)

.. py:class:: MERA(L, uni=None, iso=None, phys_dim=2, dangle=False, site_ind_id='k{}', site_tag_id='I{}', **tn_opts)

   Bases: :py:obj:`quimb.tensor.tensor_1d.TensorNetwork1DVector`, :py:obj:`quimb.tensor.tensor_1d.TensorNetwork1D`, :py:obj:`quimb.tensor.tensor_core.TensorNetwork`


   The Multi-scale Entanglement Renormalization Ansatz (MERA) state::

           ...     ...     ...     ...     ...     ...
        |       |       |       |       |       |       |
       ISO     ISO     ISO     ISO     ISO     ISO     ISO   :
          \   /   \   /   \   /   \   /   \   /   \   /      : '_LAYER1'
           UNI     UNI     UNI     UNI     UNI     UNI       :
          /   \   /   \   /   \   /   \   /   \   /   \
       O ISO ISO ISO ISO ISO ISO ISO ISO ISO ISO ISO ISO I   :
       | | | | | | | | | | | | | | | | | | | | | | | | | |   : '_LAYER0'
       UNI UNI UNI UNI UNI UNI UNI UNI UNI UNI UNI UNI UNI   :
       | | | | | | | | | | | | | | | | | | | | | | | | | |  <-- phys_dim
       0 1 2 3 4 ....                            ... L-2 L-1

   :param L: The number of phyiscal sites. Shoule be a power of 2.
   :type L: int
   :param uni: The unitary operator(s). These will be cycled over and placed from
               bottom left to top right in diagram above.
   :type uni: array or sequence of arrays of shape (d, d, d, d).
   :param iso: The isometry operator(s). These will be cycled over and placed from
               bottom left to top right in diagram above.
   :type iso: array or sequence of arrays of shape (d, d, d)
   :param phys_dim: The dimension of the local hilbert space.
   :type phys_dim: int, optional
   :param dangle: Whether to leave a dangling index on the final isometry, in order to
                  maintain perfect scale invariance, else join the final unitaries just
                  with an indentity.
   :type dangle: bool, optional


   .. py:attribute:: _EXTRA_PROPS
      :value: ('_site_ind_id', '_site_tag_id', 'cyclic', '_L')



   .. py:attribute:: _CONTRACT_STRUCTURED
      :value: False



   .. py:attribute:: _site_ind_id
      :value: 'k{}'



   .. py:attribute:: _site_tag_id
      :value: 'I{}'



   .. py:attribute:: cyclic
      :value: True



   .. py:attribute:: _L


   .. py:method:: rand(L, max_bond=None, phys_dim=2, dtype=float, **mera_opts)
      :classmethod:



   .. py:method:: rand_invar(L, phys_dim=2, dtype=float, **mera_opts)
      :classmethod:


      Generate a random translational and scale invariant MERA.