TensorFlow Quantum is a library for hybrid quantum-classical machine learning. 

#Ahybridquantum-classicalmodel.
model=tf.keras.Sequential([
#Quantumcircuitdatacomesininsideoftensors.
tf.keras.Input(shape=(),dtype=tf.dtypes.string),
#ParametrizedQuantumCircuit(PQC)providesoutput
#datafromtheinputcircuitsrunonaquantumcomputer.
tfq.layers.PQC(my_circuit,[cirq.Z(q1),cirq.X(q0)]),
#Outputdatafromquantumcomputerpassedthroughmodel.
tf.keras.layers.Dense(50)
])

TensorFlow Quantum (TFQ) is a quantum machine learning library for rapid prototyping of hybrid quantum-classical ML models. Research in quantum algorithms and applications can leverage Google’s quantum computing frameworks, all from within TensorFlow.

TensorFlow Quantum focuses on quantum data and building hybrid quantum-classical models. It integrates quantum computing algorithms and logic designed in Cirq, and provides quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators. Read more in the TensorFlow Quantum white paper.

Start with the overview, then run the notebook tutorials.