TensorFlow Quantum is a library for hybrid quantum-classical machine learning.
# A hybrid quantum-classical model. model = tf.keras.Sequential([ # Quantum circuit data comes in inside of tensors. tf.keras.Input(shape=(), dtype=tf.dtypes.string), # Parametrized Quantum Circuit (PQC) provides output # data from the input circuits run on a quantum computer. tfq.layers.PQC(my_circuit, [cirq.Z(q1), cirq.X(q0)]), # Output data from quantum computer passed through model. 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.
