Integrate the TensorFlow Quantum optimisation tools with OpenQAOA

[KilianPoirier]

Issue Description

Can we add some of TensorFlow Quantum's optimisation tools to the OpenQAOA stack?
TensorFlow Quantum is a python framework for quantum machine learning, therefore related to QAOA. It focuses on building hybrid quantum-classical models and provide tools to interleave quantum algorithms and circuit designed in Cirq with TensorFlow.

TensorFlow Quantum provides the following operations:

• Sample from output distributions of batches of circuits, see tfq.layers.PQC and tfq.layers.Sample.
• Calculate expectation value of batches of Pauli sums on batches of circuits, see tfq.layers.Expectation.
• Simulate batches of circuit and states, see tfq.layers.State.

Note: This most likely requires integration with Cirq too, for more details see #306 .

Changes to be made

In the same way we implemented different backends (physical QPU or simulators), implement a plugin package openqaoa-tfq that allows usage of TensorFlow Quantum optinmisation and simulation tools. More specifically, changes include:

• Creation of a new plugin openqaoa-tfq including all necessary components (e.g. setup.py, pyproject.toml, etc...).
• Creation of a openqaoa-tfq/backend equivalent, bridging the stack's internal representation to one compatible with TensorFlow Quantum SDK.
• Creation of unit tests to make sure all features are correctly supported.