what is Tensorflow Quantum(TFQ)?

1. what is TensorFlow Quantum?

• Similar to PyTorch, Tensorflow, TFQ is one of the python based framework used to build Quantum Machine Learning models on top of QPU by designing required Circuits and defining applicable gates and measures for the given CNN, RNN etc. models which will sits on top of designed circuits. one can design circuits using cirq

2. How it is useful with an existing Tensorflow library?

• it can be useful in following ways, traditional ML algorithms were designed in past 1960's even though if we adjust the tuned parameter, the accuracy still had some bottle necks to improve, where observe some counts in False +tives & -tives rates, so now with TFQ Libraries one can overcome the accuracy bottleneck.

Just experiment with this example:

1. Build a Classical ML model by picking one multi-label dataset and train it using most powerful gradient boosting ML algorithm named "XGBOOST" then notes its accuracy
2. Secondly build on TFQ ML model similarly train on the above dataset on 2 Qubits QPU where at each quantum measure you can observe variation in its output, because of the superposition switches which was a blend of 0's and 1's with 4 possible states and with its entanglement and finally compare score , you can find improvement with TFQ ML, model because of its internal design mechanism which was derived from Quantum mechanics it can solve more complex matrix computations

please find good amount of Quantum Machine Learning, benefits here

3. what is hybrid quantum-classical machine learning?

• Let’s take an simple example of Hybrid Reality which was a blend of Augmented and Virtual reality, so it can have both features once product ionized. Similarly, hybrid quantum-classical machine learning means which was a mix of two hybrid methods that is classical and quantum processing, so TFQ was designed based on these two methods.

I'm one of the SWEs who worked on TensorFlow Quantum. I'll do my best to answer your three questions.

1. TensorFlow Quantum is a piece of software to incorporate the quantum computing primitives in Cirq into TensorFlow in a native and scalable fashion. It is primarily targeted at researchers.

2. Quantum computing is statistical in nature, TensorFlow is very useful for (among other things) statistical things like analyzing data and building models at very large scale. Having this ability to leverage the tools of TensorFlow alongside quantum computing workflows will hopefully lead to new research at scale that might not have otherwise been possible.

3. Hybrid quantum-classical machine learning is machine learning that involves both classical and quantum data. TensorFlow Quantum was designed with the goal of developing a better understanding of quantum data.

If my answers were too short for your liking, these sorts questions are also answered in greater detail over here and here.