supermarq.qcvv.su2
==================

.. py:module:: supermarq.qcvv.su2

.. autoapi-nested-parse::

   Tooling for SU(2) benchmarking.



Classes
-------

.. autoapisummary::

   supermarq.qcvv.su2.SU2
   supermarq.qcvv.su2.SU2Results


Functions
---------

.. autoapisummary::

   supermarq.qcvv.su2.decay


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

.. py:class:: SU2(num_circuits: int, cycle_depths: collections.abc.Iterable[int], two_qubit_gate: cirq.Gate = cirq.CZ, *, qubits: collections.abc.Sequence[cirq.Qid] | None = None, random_seed: int | numpy.random.Generator | None = None, _samples: list[supermarq.qcvv.base_experiment.Sample] | None = None, **kwargs: str)

   Bases: :py:obj:`supermarq.qcvv.base_experiment.QCVVExperiment`\ [\ :py:obj:`SU2Results`\ ]


   SU2 benchmarking experiment.

   SU2 benchmarking extracts the fidelity of a given two qubit gate, even in the presence of
   additional single qubit errors. The method works by sampling circuits of the form

   .. code::

       0: ──│─Rr───Q───X───Q──│─ ^{n} ... ─│─Rr───X─│─ ^{N-n} ... ──Rf───M───
            │      │       │  │            │        │                    │
       1: ──│─Rr───Q───X───Q──│─      ... ─│─Rr───X─│─        ... ──Rf───M───

   Where each :code:`Rr` gate is a randomly chosen :math:`SU(2)` rotation and the :code:`Rf` gates
   are single qubit :math:`SU(2)` rotations that in the absence of noise invert the preceding
   circuit so that the final qubit state should be :code:`00`.

   An exponential fit decay is then fitted to the observed 00 state probability as it decays with
   the number of two qubit gates included. Note that all circuits contain a fixed number of single
   qubit gates, so that the contribution for single qubit noise is constant.

   See Fig. 3 of https://www.nature.com/articles/s41586-023-06481-y#Fig3 for further
   details.


   .. py:attribute:: two_qubit_gate

      The two qubit gate to be benchmarked


.. py:class:: SU2Results

   Bases: :py:obj:`supermarq.qcvv.base_experiment.QCVVResults`


   Data structure for the SU2 experiment results.


   .. py:method:: plot_results(filename: str | None = None) -> matplotlib.pyplot.Figure

      Plot the results of the experiment.

      :param filename: Optional argument providing a filename to save the plots to. Defaults to None,
                       indicating not to save the plot.

      :returns: A single matplotlib figure containing the relevant plots of the results data.

      :raises RuntimeError: If there is no data stored.



   .. py:property:: single_qubit_noise
      :type: float


      Returns:
      Estimated single qubit noise.


   .. py:property:: single_qubit_noise_std
      :type: float


      Returns:
      Standard deviation of estimated single qubit noise.


   .. py:property:: two_qubit_gate_error
      :type: float


      Returns:
      The two qubit gate error. Equal to one minus the fidelity.


   .. py:property:: two_qubit_gate_error_std
      :type: float


      Returns:
      The two qubit gate error standard deviation. Equal to standard deviation of the
      fidelity.


   .. py:property:: two_qubit_gate_fidelity
      :type: float


      Returns:
      Estimated two qubit gate fidelity.


   .. py:property:: two_qubit_gate_fidelity_std
      :type: float


      Returns:
      Standard deviation of estimated two qubit gate fidelity.


.. py:function:: decay(x: float, single_qubit_noise: float, two_qubit_gate_fidelity: float) -> float

   The fitting function used for SU2 benchmarking.

   :param x: The number of two qubit gates.
   :param single_qubit_noise: The single qubit noise parameter.
   :param two_qubit_gate_fidelity: The two qubit gate fidelity parameter.

   :returns: The expected probability of measuring the ``|00>`` state.