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Tensorflow Quantum - A hybrid quantum classical model using tensors

https://www.tensorflow.org/quantum


# 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
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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 dataand 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. Whitepaper here: https://arxiv.org/pdf/2003.02989.pdf



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