Qnetvo

Non-Breaking Changes

* The Pennylane dependency was updated to the most recent version (v0.37)
* Docs dependencies on `m2r2` were removed and replaced with `sphinx-mdinclude`
* The sphinx build action was removed from the project's github action workflows due to sphinx being v2 rather than v8. A custom docs build command using the most recent sphinx version was used instead.
* All qnetvo issues related to this update were resolved.

* Added Demos for training quantum networking protocols using classical and quantum communications
* Added support for `qml.AdamOptimizer` in `qnetvo.gradient_descent`
* Fixed bug in `qnetvo.behavior_fn` where multiple layers were not handled properly.

Updates:

- PennyLane has been upgraded to the most recent version (v0.29.1)

Non-breaking changes:

- Flaky unit test failures are rerun on failure for stability
- File-IO functionality broken in v0.3 release is fixed so that optimization dictionaries can be written and read from json.

In this release we add a few new utilities and ansatzes.
* `state_vec_fn` : constructs a function that obtains the vector representation of a quantum state output from a circuit.
* `density_mat_fn` : constructs a function that obtains the density matrix representation of a quantum state output from a circuit.
* `W_state` : Initializes the 3-qubit W state
* `nonmax_entangled_state` : initializes a GHZ-like state that is nonmaximally entangled.
* `shared_coin_flip_state`: generates a mixed state that mimics the a biased coinflip shared between multiple parties.
* `graph_state_fn` : generates a circuit that initializes a pre-specified graph state.

In this release we are pleased to announce that local operations and classical communication (LOCC) is now supported by qNetVO simulation and variational optimization software. This new functionality allows users to implement and optimize protocols such as teleportation, entanglement swapping, entanglement distillation, and many more!

Our new features allow for `CCSender` nodes to measure a quantum state and broadcast the classical result. A `CCReceiver` node can then receive the broadcast and use the classical information to condition an input. The midcircuit measurements and conditioned operations are implemented from trainable unitary circuits.

Modifications:

- Restructured network node class hierarchy and introduce `CCSender` and CCReceiver` network nodes.
- Added support for classical communication within the `NetworkAnsatz` class is considered.
- Update qNetVO to use the most recent version of PennyLane (v0.28.0)

Breaking Changes:

Minor breaking changes are incorporated in this release. In particular, the attributes of the `NetworkAnsatz`.

More generally, we observe that the parallelized gradient evaluation available for the CHSH and nlocal star/chain cost functions becomes flaky in PennyLane v0.28. If utilizing the parallel gradient evaluation methods, consider using qNetVO v0.3.0 and PennyLane v0.27.0 for improved stability.