Release 0.2.0

VERSION

@@ -1 +1 @@
-0.1.0.dev
+0.2.0

doc/changelog.rst

@@ -1,10 +1,62 @@
-*********
 Changelog
-*********
+=========
 
+Version 0.2.0 (Mar 23, 2026)
+----------------------------
 
-Version 0.0.2 (Oct 04, 2024)
-+++++++++++++++++++++++++++++++++
+Bug Fixes
+^^^^^^^^^
+
+Reinforcement learning module (``src/qutip_qoc/_rl.py``):
+
+- Corrections in algorithm execution time #31
+
+JOPT
+
+- Fix: Enable JOPT to support open-system optimization with TRACEDIFF fidelity #49
+        - This PR resolves an issue where JOPT failed to optimize open quantum systems using TRACEDIFF fidelity due to incompatibilities between JAX autodiff and Qobj data structures.
+
+GRAPE
+
+- Match grape infidelity with manually computed one #51 (fixes #46):
+        - With this PR the GRAPE-reported infidelity matches the manually computed one by evolving the system using the optimized control pulses.
+
+GitHub Workflows
+
+- Update versions of action tools #35
+
+Pulse optimisation and objective modules
+
+- Fix state transfer not working for GRAPE and CRAB #36 (fixes #34)
+
+Documentation
+^^^^^^^^^^^^^
+
+- Fixing broken links in README #39
+- Fix: Load jQuery explicitly to resolve broken search panel on deployed docs #42
+
+Miscellaneous
+^^^^^^^^^^^^^
+
+- Add interactive test notebooks for closed systems #43
+- Added requirements for interactive tests to setup.cfg #53
+- Make it possible to display qutip-qoc as a family package #56
+
+Dependencies management
+^^^^^^^^^^^^^^^^^^^^^^^
+
+- Make all JAX and machine learning related dependencies optional #32
+
+Dependabot dependencies upgrades
+
+- #25
+- #26
+- #27
+- #28
+- #45
+
+Version 0.1.1 (Oct 04, 2024)
+----------------------------
 
 This is an update to the beta release of ``qutip-qoc``.
 
@@ -16,19 +68,19 @@ It mainly introduces the new reinforcement learning algorithm ``qutip_qoc._rl``.
 - 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)
+- Prevent loss of ``__time__`` keyword in ``optimize_pulses`` (#22, by flowerthrower)
 
 
-Version 0.0.1 (May xx, 2024)
-+++++++++++++++++++++++++++++++++
+Version 0.1.0b1 (July, 2024)
+----------------------------
 
 This is the beta release of ``qutip-qoc``, the extended quantum control package in QuTiP.
 
@@ -40,14 +92,14 @@ It has undergone major refactoring and restructuring of the codebase.
 - 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
 
 
 Version 0.0.0 (December 26, 2023)
-+++++++++++++++++++++++++++++++++
+---------------------------------
 
 This is the alpha version of ``qutip-qoc``, the extended quantum control package in QuTiP.
 
@@ -56,16 +108,15 @@ It enhances it by providing two additional algorithms to optimize analytically d
 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.
+  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.
-
+  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