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qsttoolkit.tomography Subpackage
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This subpackage contains models for performing quantum state tomography. Each model is implemented in its own class, each inheriting basic performance analysis and plotting functions from a parent class `qsttoolkit.tomography.QST`.

The fundamental aim of QSTToolkit is to provide modular, 'drag-and-drop' functions for researching, testing and comparing quantum state tomography methods in different experimental situations. This is enabled by the `CustomQuantumStateTomography` class, which allows a user to define a single pass of their own custom tomography training loop. Experimentation with combinations of tomography components is encouraged - for example, using a generator model with a different density matrix parametrization and loss function.

Traditional QST
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Traditional quantum state tomography is implemented using Maximum Likelihood Estimation (MLE):

.. toctree::
   :maxdepth: 2

   qsttoolkit.tomography.tradqst.MLE_reconstructor

Deep Learning QST
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Four deep learning models are currently implemented using `TensorFlow <https://www.tensorflow.org/>`_: one for quantum state discrimination and three for quantum state tomography. Each model has a dedicated class:

.. toctree::
   :maxdepth: 2

   qsttoolkit.tomography.dlqst.CNN_classifier
   qsttoolkit.tomography.dlqst.GAN_reconstructor
   qsttoolkit.tomography.dlqst.multitask_reconstructor

Global QST Utility Functions
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Functions used in both traditional and deep learning methods for quantum state tomography.

.. automodule:: qsttoolkit.tomography.QST
   :members:
   :undoc-members:
   :show-inheritance:

Loss Functions
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.. automodule:: qsttoolkit.tomography.loss
   :members:
   :undoc-members:
   :show-inheritance: