tfq.convert_to_tensor

Convert lists of tfq supported primitives to tensor representations.

tfq.convert_to_tensor(
    items_to_convert, deterministic_proto_serialize=False
)

Used in the notebooks

Used in the tutorials
Hello, many worlds Quantum Convolutional Neural Network Calculate gradients MNIST classification Quantum data

Recursively convert a nested lists of cirq.PauliSum or cirq.Circuit objects to a tf.Tensor representation. Note that cirq serialization only supports cirq.GridQubits so we also require that input circuits and pauli sums are defined only on cirq.GridQubits.

my_qubits = cirq.GridQubit.rect(1, 2)
my_circuits = [cirq.Circuit(cirq.X(my_qubits[0])),
               cirq.Circuit(cirq.Z(my_qubits[0]))
]
tensor_input = tfq.convert_to_tensor(my_circuits)
# Now tensor_input can be used as model input etc.
same_circuits = tfq.from_tensor(tensor_input)
# same_circuits now holds cirq.Circuit objects once more.
same_circuits
[cirq.Circuit([
    cirq.Moment(operations=[
        cirq.X.on(cirq.GridQubit(0, 0)),
    ]),
])
 cirq.Circuit([
    cirq.Moment(operations=[
        cirq.Z.on(cirq.GridQubit(0, 0)),
    ]),
])]

Args
items_to_convert	Python list or nested list of cirq.Circuit or cirq.Paulisum objects. Must be recangular.
deterministic_proto_serialize	Whether to use a deterministic serialization when calling SerializeToString().

Returns
A tf.Tensor that represents the input items.

Raises
TypeError	In case of invalid arguments provided in items_to_convert.