:py:mod:`pygsti.extras.devices.experimentaldevice`
==================================================

.. py:module:: pygsti.extras.devices.experimentaldevice

.. autoapi-nested-parse::

   Functions for interfacing pyGSTi with external devices, including IBM Q and Rigetti 






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

Classes
~~~~~~~

.. autoapisummary::

   pygsti.extras.devices.experimentaldevice.ExperimentalDevice




.. py:class:: ExperimentalDevice(qubits, graph, gate_mapping=None)


   Bases: :py:obj:`object`

   Specification of an experimental device.
       

   Initialize an IBMQ device from qubits and connectivity info.

   Parameters
   ----------
   qubits: list
       Qubit labels

   graph: QubitGraph
       QubitGraph depicting device connectivity.

   gate_mapping: dict, optional
       Mapping between pyGSTi gate names (keys) and IBM native gates (values).
       If None, simply use {'Gcnot': 'cx'} to recover legacy behavior.

   .. py:attribute:: qubits

      

   .. py:attribute:: graph

      

   .. py:attribute:: gate_mapping
      :value: 'None'

      

   .. py:method:: from_qiskit_backend(backend, gate_mapping=None)
      :classmethod:

      Construct a ExperimentalDevice from Qiskit provider backend information.

      Provider backends can be obtained via:
          IBMQ.load_account()
          provider = IBMQ.get_provider() # with potential optional kwargs
          backend = provider.get_backend(<device name>)

      Parameters
      ----------
      backend: IBMQBackend
          Backend obtained from IBMQ

      gate_mapping: dict, optional
          Mapping between pyGSTi gate names (keys) and IBM native gates (values).
          If None, simply use {'Gcnot': 'cx'} to recover legacy behavior.

      Returns
      -------
      Initialized ExperimentalDevice


   .. py:method:: from_legacy_device(devname)
      :classmethod:

      Create a ExperimentalDevice from a legacy pyGSTi pygsti.extras.devices module.

      Parameters
      ----------
      devname: str
          Name of the pygsti.extras.devices module to use

      Returns
      -------
      Initialized ExperimentalDevice


   .. py:method:: create_processor_spec(gate_names=None, qubit_subset=None, subset_only=False, remove_edges=None)

      Create a QubitProcessorSpec from user-specified gates and device connectivity.

      Parameters
      ----------
      gate_names: list of str
          List of one-qubit and two-qubit gate names. If None, use the keys of self.gate_mapping.

      qubit_subset: list
          A subset of qubits to include in the processor spec. If None, use self.qubits.

      subset_only: bool
          Whether or not to include all the device qubits in the processor spec (False, default)
          or just qubit_subset (True).

      remove_edges: list
          A list of edges to drop from the connectivity graph.

      Returns
      -------
      The created QubitProcessorSpec


   .. py:method:: create_error_rates_model(caldata=None, calformat='ibmq-v2019', model_type='TwirledLayers', idle_name=None)

      Create an error rates model (OplessModel) from calibration data.

      Parameters
      ----------
      caldata: dict
          Calibration data. Currently, this can be retrieved via
          `backend.properties().to_dict()`.

      calformat: One of ['ibmq-v2018', 'ibmq-v2019', 'rigetti', 'native']
          Calibration data format, defaults to ibmq-v2019. TODO: It seems this has
          been changed, what version are we actually on?

      model_type: One of ['TwirledLayers', 'TwirledGates', 'AnyErrorCausesFailure', 'AnyErrorCausesRandomOutput']
          Type of OplessModel to create

      idle_name: str
          Name for the idle gate

      Returns
      -------
      OplessModel


   .. py:method:: create_local_depolarizing_model(caldata=None, calformat='ibmq-v2019', qubits=None)

      Create a LocalNoiseModel with depolarizing noise based on calibration data.

      Note: this model is *** NOT *** suitable for optimization: it is not aware that it is a local depolarization
      with non-independent error rates model.

      Parameters
      ----------
      caldata: dict
          Calibration data. Currently, this can be retrieved via
          `backend.properties().to_dict()`.

      calformat: One of ['ibmq-v2018', 'ibmq-v2019', 'rigetti', 'native']
          Calibration data format, defaults to ibmq-v2019. TODO: It seems this has
          been changed, what version are we actually on?

      qubits: list
          Qubit labels to include in the model

      Returns
      -------
      OplessModel