Qutip-qoc

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This is an update to the beta release of ``qutip-qoc``.

It mainly introduces the new reinforcement learning algorithm ``qutip_qoc._rl``.

- Non-public facing functions have been renamed to start with an underscore.
- As with other QuTiP functions, ``optimize_pulses`` now takes a ``tlist`` argument instead of ``_TimeInterval``.
- The structure for the control guess and bounds has changed and now takes in an optional ``__time__`` keyword.
- The ``result`` does no longer return ``optimized_objectives`` but instead ``optimized_H``.

Features
--------

- New reinforcement learning algorithm, developed during GSOC24 (19, 18, by LegionAtol)
- Automatic transfromation of initial and target operator to superoperator (23, by flowerthrower)

Bug Fixes
---------

- Prevent loss of `__time__` keyword in optimize_pulses (22, by flowerthrower)

+++++++++++++++++++++++++++++++++

This is the beta release of ``qutip-qoc``, the extended quantum control package in QuTiP.

It has undergone major refactoring and restructuring of the codebase.

- Non-public facing functions have been renamed to start with an underscore.
- As with other QuTiP functions, ``optimize_pulses`` now takes a ``tlist`` argument instead of ``_TimeInterval``.
- The structure for the control guess and bounds has changed and now takes in an optional ``__time__`` keyword.
- The ``result`` does no longer return ``optimized_objectives`` but instead ``optimized_H``.

Bug Fixes
---------

- basinhopping result does not contain minimizer message
- boundary issues with CRAB

+++++++++++++++++++++++++++++++++

This is the alpha version of ``qutip-qoc``, the extended quantum control package in QuTiP.

The ``qutip-qoc`` package builds up on the ``qutip-qtrl`` `package <https://github.com/qutip/qutip-qtrl>`_.
It enhances it by providing two additional algorithms to optimize analytically defined control functions.
The package also aims for a more general way of defining control problems with QuTiP and makes switching between the four control algorithms very easy.

Features
--------

- ``qutip_qoc.GOAT`` is an extension to the Gradient Optimization of Analytic conTrols (GOAT) :cite:`GOAT` algorithm.
It encoporates an additional time parameterization to allow for optimization over the total evolution time.
- ``qutip_qoc.JOPT`` is an JAX automatic differentiation Optimization of Analytic conTrols (JOPT) algorithm.
- Both algorithms can be addressed by the ``qutip_qoc.optimize_pulses`` function, which consists of a two-layer approach to find global optimal values for parameterized analytical control functions.
The global optimization layer provides ``scipy.optimize.dual_annealing`` and ``scipy.optimize.basinhopping``, while the local minimization layer supports all gradient driven ``scipy.optimize.minimize`` methods.


Bug Fixes
---------

- None