v0.10 changelog (#1402)

**Context:** Changelog

---------

Co-authored-by: Joey Carter <joseph.carter@xanadu.ai>

Author: isaacdevlugt

doc/releases/changelog-dev.md

@@ -2,156 +2,214 @@
 
 <h3>New features since last release</h3>
 
-* Catalyst can now load local MLIR plugins from python. Including support for `entry_points`.
+* Catalyst can now load and apply local MLIR plugins from the PennyLane frontend.
   [(#1317)](https://github.com/PennyLaneAI/catalyst/pull/1317)
   [(#1361)](https://github.com/PennyLaneAI/catalyst/pull/1361)
   [(#1370)](https://github.com/PennyLaneAI/catalyst/pull/1370)
 
+  Custom compilation passes and dialects in MLIR can be specified for use in Catalyst via a shared 
+  object (`*.so` or `*.dylib` on MacOS) that implements the pass. Details on creating your own 
+  plugin can be found in our 
+  [compiler plugin documentation](https://docs.pennylane.ai/projects/catalyst/en/stable/dev/plugins.html).
+  At a high level, there are three ways to utilize a plugin once it's properly specified:
+
+  * :func:`~.passes.apply_pass` can be used on QNodes when there is a  
+    [Python entry point](https://packaging.python.org/en/latest/specifications/entry-points/) 
+    defined for the plugin.
+
+    ```python
+    @catalyst.passes.apply_pass(pass_name)
+    @qml.qnode(qml.device("lightning.qubit", wires=1))
+    def qnode():
+        return qml.state()
+
+    @qml.qjit
+    def module():
+        return qnode()
+    ```
+
+  * :func:`~.passes.apply_pass_plugin` can be used on QNodes when there is not an entry point 
+    defined for the plugin.
+
+    ```python
+    @catalyst.passes.apply_pass_plugin(path_to_plugin, pass_name)
+    @qml.qnode(qml.device("lightning.qubit", wires=1))
+    def qnode():
+        return qml.state()
+
+    @qml.qjit
+    def module():
+        return qnode()
+    ```
+
+  * Specifying multiple compilation pass plugins or dialect plugins directly in :func:`~.qjit` via 
+    the `pass_plugins` and `dialect_plugins` keyword arguments, which must be lists of plugin paths.
+
+    ```python
+    from pathlib import Path
+
+    plugin = Path("shared_object_file.so")
+
+    @qml.qnode(qml.device("lightning.qubit", wires=0))
+    def qnode():
+      qml.Hadamard(wires=0)
+      return qml.state()
+
+    @qml.qjit(pass_plugins=[plugin], dialect_plugins=[plugin])
+    def module():
+      return catalyst.passes.apply_pass(qnode, "pass_name")()
+    ```
+
+  For more information on usage, 
+  visit our [compiler plugin documentation](https://docs.pennylane.ai/projects/catalyst/en/stable/dev/plugins.html).
+
 <h3>Improvements 🛠</h3>
 
-* Lightning runtime shot-measurement support for Hermitian observables.
+* The lightning runtime now supports finite shots with measuring expectation values of `qml.Hermitian`.
   [(#451)](https://github.com/PennyLaneAI/catalyst/pull/451)
 
-* Replace pybind11 with nanobind for C++/Python bindings in the frontend and in the runtime.
+* Pybind11 has been replaced with nanobind for C++/Python bindings in the frontend and in the runtime.
   [(#1173)](https://github.com/PennyLaneAI/catalyst/pull/1173)
   [(#1293)](https://github.com/PennyLaneAI/catalyst/pull/1293)
   [(#1391)](https://github.com/PennyLaneAI/catalyst/pull/1391)
 
   Nanobind has been developed as a natural successor to the pybind11 library and offers a number of
-  [advantages](https://nanobind.readthedocs.io/en/latest/why.html#major-additions), in particular,
-  its ability to target Python's [stable ABI interface](https://docs.python.org/3/c-api/stable.html)
-  starting with Python 3.12.
+  [advantages](https://nanobind.readthedocs.io/en/latest/why.html#major-additions) like its ability 
+  to target Python's [stable ABI interface](https://docs.python.org/3/c-api/stable.html) starting 
+  with Python 3.12.
 
-* Catalyst now uses the new compiler API to compile quantum code from the python frontend.
-  Frontend no longer uses pybind11 to connect to the compiler. Instead, it uses subprocess instead.
+* Catalyst now uses the new compiler API (`catalyst-cli`) to compile quantum code from the Python 
+  frontend instead of using pybind11 as an interface between the compiler and the frontend. 
   [(#1285)](https://github.com/PennyLaneAI/catalyst/pull/1285)
 
-* Add a MLIR decomposition for the gate set {"T", "S", "Z", "Hadamard", "RZ", "PhaseShift", "CNOT"}
-  to the gate set {RX, RY, MS}. It is useful for trapped ion devices. It can be used thanks to
-  `quantum-opt --ions-decomposition`.
+* Gates in the gate set `{T, S, Z, Hadamard, RZ, PhaseShift, CNOT}` now have MLIR decompositions to 
+  the gate set `{RX, RY, MS}`, which are useful for trapped ion devices.
   [(#1226)](https://github.com/PennyLaneAI/catalyst/pull/1226)
 
-* qml.CosineWindow is now compatible with QJIT.
+* `qml.CosineWindow` is now compatible with QJIT.
   [(#1166)](https://github.com/PennyLaneAI/catalyst/pull/1166)
 
 * All PennyLane templates are tested for QJIT compatibility.
   [(#1161)](https://github.com/PennyLaneAI/catalyst/pull/1161)
 
-* Decouple Python from the Runtime by using the Python Global Interpreter Lock (GIL) instead of
-  custom mutexes.
+* Python is now decoupled from the Runtime by using the Python Global Interpreter Lock (GIL) instead 
+  of custom mutexes.
   [(#624)](https://github.com/PennyLaneAI/catalyst/pull/624)
 
   In addition, executables created using :func:`~.debug.compile_executable` no longer require
   linking against Python shared libraries after decoupling Python from the Runtime C-API.
   [(#1305)](https://github.com/PennyLaneAI/catalyst/pull/1305)
 
-* Improves the readability of conditional passes in pipelines
+* The readability of conditional passes in `catalyst.pipelines` has been improved.
   [(#1194)](https://github.com/PennyLaneAI/catalyst/pull/1194)
 
-* Cleans up the output of compiler instrumentation.
+* The output of compiler instrumentation has been cleaned up by only printing stats after a `pipeline`. 
+  It is still possible to get the more detailed output with `qjit(verbose=True)`.
   [(#1343)](https://github.com/PennyLaneAI/catalyst/pull/1343)
 
-* Generate stable ABI wheels for Python 3.12 and up.
+* Stable ABI wheels for Python 3.12 and up are now generated.
   [(#1357)](https://github.com/PennyLaneAI/catalyst/pull/1357)
   [(#1385)](https://github.com/PennyLaneAI/catalyst/pull/1385)
 
-* A new circuit optimization pass, `--disentangle-CNOT`, is available.
+* Two new circuit optimization passes, `--disentangle-CNOT` and `--disentangle-SWAP`, are available.
   [(#1154)](https://github.com/PennyLaneAI/catalyst/pull/1154)
 
-  The pass disentangles CNOT gates whenever possible, e.g. when the control bit
-  is known to be in |0>, the pass removes the CNOT. The pass uses a finite state
-  machine to propagate simple one-qubit states, in order to determine
-  the input states to the CNOT.
-
-  The algorithm is taken from [Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits, by Ji Liu, Luciano Bello, and Huiyang Zhou](https://arxiv.org/abs/2012.07711).
-
-* A new circuit optimization pass, `--disentangle-SWAP`, is available.
-  [(#1297)](https://github.com/PennyLaneAI/catalyst/pull/1297)
+  The CNOT pass disentangles CNOT gates whenever possible, e.g., when the control bit is known to be 
+  in the `|0>` state, the pass removes the CNOT. The pass uses a finite state machine to propagate 
+  simple one-qubit states, in order to determine the input states to the CNOT.
+  
+  Similarly, the SWAP pass disentangles SWAP gates whenever possible by using a finite state machine 
+  to propagate simple one-qubit states, similar to the `--disentangle-CNOT` pass.
 
-  The pass disentangles SWAP gates whenever possible by using a finite state
-  machine to propagate simple one-qubit states, similar to the `--disentangle-CNOT` pass.
-
-  The algorithm is taken from [Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits, by Ji Liu, Luciano Bello, and Huiyang Zhou](https://arxiv.org/abs/2012.07711).
+  Both passes are implemented in accordance with the algorithm from 
+  J. Liu, L. Bello, and H. Zhou, _Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits_, 2020, [arXiv:2012.07711](https://arxiv.org/abs/2012.07711) [quant-ph].
 
 * Allow specifying a branch to switch to when setting up a dev environment from the wheels.
   [(#1406)](https://github.com/PennyLaneAI/catalyst/pull/1406)
 
 <h3>Breaking changes 💔</h3>
 
-* The `sample` and `counts` measurement primitives now support dynamic shot values across catalyst, although at the PennyLane side, the device shots still is constrained to a static integer literal.
+* The `sample` and `counts` measurement primitives now support dynamic shot values across Catalyst, 
+  although, on the PennyLane side, the device's shots is still constrained to a static integer 
+  literal.
+  [(#1310)](https://github.com/PennyLaneAI/catalyst/pull/1310)
 
-  To support this, `SampleOp` and `CountsOp` in mlir no longer carry the shots attribute, since integer attributes are tied to literal values and must be static.
+  To support this, `SampleOp` and `CountsOp` in MLIR no longer carry the shots attribute, since 
+  integer attributes are tied to literal values and must be static.
 
-  `DeviceInitOp` now takes in an optional SSA argument for shots, and the device init runtime CAPI will take in this SSA shots value as an argument and set it as the device shots.
-  The sample and counts runtime CAPI functions no longer take in the shots argument and will retrieve shots from the device.
+  `DeviceInitOp` now takes in an optional SSA argument for shots, and the device init runtime CAPI 
+  will take in this SSA shots value as an argument and set it as the device shots. The sample and 
+  counts runtime CAPI functions no longer take in the shots argument and will retrieve shots from 
+  the device.
 
-  Correspondingly, the device C++ interface should no longer parse the `DeviceInitOp`'s attributes dictionary for the shots.
-  For now we still keep the shots as an attribute so device implementors can have time to migrate, but we will remove shots from the attribute dictionary in the next release.
-
-  [(#1170)](https://github.com/PennyLaneAI/catalyst/pull/1170)
-  [(#1310)](https://github.com/PennyLaneAI/catalyst/pull/1310)
+  Correspondingly, the device C++ interface should no longer parse the `DeviceInitOp`'s attributes 
+  dictionary for the shots. For now, we still keep the shots as an attribute so device implementors 
+  can have time to migrate, but we will remove shots from the attribute dictionary in the next 
+  release (`v0.11`)
 
 * The `toml` module has been migrated to PennyLane with an updated schema for declaring device
   capabilities. Devices with TOML files using `schema = 2` will not be compatible with the latest
-  Catalyst. See [Custom Devices](https://docs.pennylane.ai/projects/catalyst/en/stable/dev/custom_devices.html)
-  for updated instructions on integrating your device with Catalyst and PennyLane
+  Catalyst. See the [Custom Devices documentation page](https://docs.pennylane.ai/projects/catalyst/en/stable/dev/custom_devices.html)
+  for updated instructions on integrating your device with Catalyst and PennyLane.
   [(#1275)](https://github.com/PennyLaneAI/catalyst/pull/1275)
 
 * Handling for the legacy operator arithmetic (the `Hamiltonian` and `Tensor` classes in PennyLane)
-  is removed.
+  has been removed.
   [(#1308)](https://github.com/PennyLaneAI/catalyst/pull/1308)
 
 <h3>Bug fixes 🐛</h3>
 
-* Fix bug introduced in 0.8 that breaks nested invocations of `qml.adjoint` and `qml.ctrl`.
+* Fixed a bug introduced in 0.8 that breaks nested invocations of `qml.adjoint` and `qml.ctrl` (e.g., 
+  `qml.adjoint(qml.adjoint(qml.H(0)))`).
   [(#1301)](https://github.com/PennyLaneAI/catalyst/issues/1301)
 
-* Fix a bug in `debug.compile_executable` which would generate incorrect stride information for
+* Fixed a bug in :func:`~.debug.compile_executable` that would generate incorrect stride information for
   array arguments of the function, in particular when non-64bit datatypes are used.
   [(#1338)](https://github.com/PennyLaneAI/catalyst/pull/1338)
 
-<h3>Deprecations 👋</h3>
-
 <h3>Internal changes ⚙️</h3>
 
-* Catalyst no longer depends on or pins the `scipy` package, instead OpenBLAS is sourced directly
-  from [`scipy-openblas32`](https://pypi.org/project/scipy-openblas32/) or Accelerate is used.
+* Catalyst no longer depends on or pins the `scipy` package. Instead, OpenBLAS is sourced directly
+  from [`scipy-openblas32`](https://pypi.org/project/scipy-openblas32/) or 
+  [Accelerate](https://developer.apple.com/accelerate/) is used.
   [(#1322)](https://github.com/PennyLaneAI/catalyst/pull/1322)
   [(#1328)](https://github.com/PennyLaneAI/catalyst/pull/1328)
 
-* The `QuantumExtension` module (previously implemented with pybind11) has been removed. This module
+* The `QuantumExtension` module—previously implemented with pybind11—has been removed. This module
   was not included in the distributed wheels and has been deprecated to align with our adoption of
   Python's stable ABI, which pybind11 does not support.
   [(#1187)](https://github.com/PennyLaneAI/catalyst/pull/1187)
 
-* Remove Lightning Qubit Dynamic plugin from Catalyst.
+* Code for using `lightning.qubit` with Catalyst has been moved from the Catalyst repository to 
+  the [Lightning repository](https://github.com/PennyLaneAI/pennylane-lightning) so that Catalyst
+  wheels will build faster.
   [(#1227)](https://github.com/PennyLaneAI/catalyst/pull/1227)
   [(#1307)](https://github.com/PennyLaneAI/catalyst/pull/1307)
   [(#1312)](https://github.com/PennyLaneAI/catalyst/pull/1312)
 
-* `catalyst-cli` and `quantum-opt` are compiled with `default` visibility, which allows for MLIR plugins to work.
+* `catalyst-cli` and `quantum-opt` are now compiled with `default` visibility, which allows for MLIR 
+  plugins to work.
   [(#1287)](https://github.com/PennyLaneAI/catalyst/pull/1287)
 
-* Sink patching of autograph's allowlist.
+* The patching mechanism of autograph's `allowlist` has been streamlined to only be used in places 
+  where it's required.
   [(#1332)](https://github.com/PennyLaneAI/catalyst/pull/1332)
   [(#1337)](https://github.com/PennyLaneAI/catalyst/pull/1337)
 
-* Each qnode now has its own transformation schedule.
-  Instead of relying on the name of the qnode, each qnode now has a transformation module,
-  which denotes the transformation schedule, embedded in its MLIR representation.
+* Each qnode now has its own transformation schedule. Instead of relying on the name of the qnode, 
+  each qnode now has a transformation module, which denotes the transformation schedule, embedded in 
+  its MLIR representation.
   [(#1323)](https://github.com/PennyLaneAI/catalyst/pull/1323)
 
-* The `apply_registered_pass_p` primitive is removed. The API for scheduling passes
-  to run using the transform dialect has been refactored. In particular,
-  passes are appended to a tuple as they are being registered and they will
-  be run in order. If there are no local passes, the global `pass_pipeline` is
-  scheduled. Furthermore, this commit also reworks the caching mechanism for
-  primitives, which is important as qnodes and functions are primitives and
-  now that we can apply passes to them, they are distinct based on which
-  passes have been scheduled to run on them.
+* The `apply_registered_pass_p` primitive has been removed and the API for scheduling passes to run 
+  using the transform dialect has been refactored. In particular, passes are appended to a tuple as 
+  they are being registered and they will be run in order. If there are no local passes, the global 
+  `pass_pipeline` is scheduled. Furthermore, this commit also reworks the caching mechanism for 
+  primitives, which is important as qnodes and functions are primitives and now that we can apply 
+  passes to them, they are distinct based on which passes have been scheduled to run on them.
   [(#1317)](https://github.com/PennyLaneAI/catalyst/pull/1317)
 
-* Replace Python C-API calls with Stable ABI calls.
+* Python C-API calls have been replaced with Stable ABI calls.
   [(#1354)](https://github.com/PennyLaneAI/catalyst/pull/1354)
 
 * A framework for loading and interacting with databases containing hardware information and
@@ -176,7 +234,8 @@
   Support for OQD devices is still under development, therefore the OQD modules are currently not
   included in the distributed wheels.
 
-* `expval` and `var` operations no longer keep the static shots attribute, as a step towards supporting dynamic shots across catalyst.
+* As a step towards supporting dynamic shots across catalyst, `expval` and `var` operations no 
+  longer keep the static shots attribute.
   [(#1360)](https://github.com/PennyLaneAI/catalyst/pull/1360)
 
 * A new `ion` dialect has been added for Catalyst programs targeting OQD trapped-ion quantum devices.
@@ -189,25 +248,28 @@
 
   A new pass, `--quantum-to-ion`, has also been added to convert logical gate-based circuits in the
   Catalyst `quantum` dialect to laser pulse operations in the `ion` dialect. This pass accepts
-  logical quantum gates from the set {RX, RY, Mølmer–Sørensen (MS)}. Doing so enables the insertion
-  of physical device parameters into the IR, which will be necessary when lowering to OQD's backend
-  calls. The physical parameters are read in from [TOML](https://toml.io/en/) files during the
-  `--quantum-to-ion` conversion. The TOML files are assumed to exist by the pass (the paths to the
-  TOML file locations are taken in as pass options), with the intention that they are generated
-  immediately before compilation during hardware-calibration runs.
-
-* IR is now extended to support literal values as opposed to SSA Values for static parameters of
-  quantum gates by adding a new gate called StaticCustomOp with lowering to regular customOp.
+  logical quantum gates from the set `{RX, RY, MS}`, where `MS` is the Mølmer–Sørensen gate. Doing 
+  so enables the insertion of physical device parameters into the IR, which will be necessary when 
+  lowering to OQD's backend calls. The physical parameters are read in from 
+  [TOML](https://toml.io/en/) files during the `--quantum-to-ion` conversion. The TOML files are 
+  assumed to exist by the pass (the paths to the TOML file locations are taken in as pass options), 
+  with the intention that they are generated immediately before compilation during 
+  hardware-calibration runs.
+
+* The Catalyst IR has been extended to support literal values as opposed to SSA Values for static 
+  parameters of quantum gates by adding a new gate called `StaticCustomOp` with lowering to regular 
+  `CustomOp`.
   [(#1387)](https://github.com/PennyLaneAI/catalyst/pull/1387)
 
 <h3>Documentation 📝</h3>
 
 * A new tutorial going through how to write a new MLIR pass is available. The tutorial writes an
-  empty pass that prints hello world. The code of the tutorial is at
+  empty pass that prints `hello world`. The code for the tutorial is located in
   [a separate github branch](https://github.com/PennyLaneAI/catalyst/commit/ba7b3438667963b307c07440acd6d7082f1960f3).
   [(#872)](https://github.com/PennyLaneAI/catalyst/pull/872)
 
-* Updated catalyst-cli documentation to reflect the removal of func-name option for trasnformation passes.
+* The `catalyst-cli` documentation has been updated to reflect the removal of the `func-name` option 
+  for transformation passes.
   [(#1368)](https://github.com/PennyLaneAI/catalyst/pull/1368)
 
 <h3>Contributors ✍️</h3>
@@ -219,7 +281,7 @@ Joey Carter,
 David Ittah,
 Erick Ochoa Lopez,
 Mehrdad Malekmohammadi,
-William Maxwell
+William Maxwell,
 Romain Moyard,
 Shuli Shu,
 Ritu Thombre,