tfq.convert_to_tensor
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Convert lists of tfq supported primitives to tensor representations.
tfq.convert_to_tensor(
items_to_convert, deterministic_proto_serialize=False
)
Used in the notebooks
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.
|
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Last updated 2024-05-17 UTC.
[null,null,["Last updated 2024-05-17 UTC."],[],[],null,["# tfq.convert_to_tensor\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/quantum/blob/v0.7.3/tensorflow_quantum/python/util.py#L263-L333) |\n\nConvert lists of tfq supported primitives to tensor representations. \n\n tfq.convert_to_tensor(\n items_to_convert, deterministic_proto_serialize=False\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Hello, many worlds](https://www.tensorflow.org/quantum/tutorials/hello_many_worlds) - [Quantum Convolutional Neural Network](https://www.tensorflow.org/quantum/tutorials/qcnn) - [Calculate gradients](https://www.tensorflow.org/quantum/tutorials/gradients) - [MNIST classification](https://www.tensorflow.org/quantum/tutorials/mnist) - [Quantum data](https://www.tensorflow.org/quantum/tutorials/quantum_data) |\n\nRecursively convert a nested lists of [`cirq.PauliSum`](https://quantumai.google/reference/python/cirq/PauliSum) or [`cirq.Circuit`](https://quantumai.google/reference/python/cirq/Circuit)\nobjects to a [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor) representation. Note that cirq serialization only\nsupports [`cirq.GridQubit`](https://quantumai.google/reference/python/cirq/GridQubit)s so we also require that input circuits and\npauli sums are defined only on [`cirq.GridQubit`](https://quantumai.google/reference/python/cirq/GridQubit)s. \n\n my_qubits = cirq.GridQubit.rect(1, 2)\n my_circuits = [cirq.Circuit(cirq.X(my_qubits[0])),\n cirq.Circuit(cirq.Z(my_qubits[0]))\n ]\n tensor_input = tfq.convert_to_tensor(my_circuits)\n # Now tensor_input can be used as model input etc.\n same_circuits = tfq.from_tensor(tensor_input)\n # same_circuits now holds cirq.Circuit objects once more.\n same_circuits\n [cirq.Circuit([\n cirq.Moment(operations=[\n cirq.X.on(cirq.GridQubit(0, 0)),\n ]),\n ])\n cirq.Circuit([\n cirq.Moment(operations=[\n cirq.Z.on(cirq.GridQubit(0, 0)),\n ]),\n ])]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `items_to_convert` | Python `list` or nested `list` of [`cirq.Circuit`](https://quantumai.google/reference/python/cirq/Circuit) or `cirq.Paulisum` objects. Must be recangular. |\n| `deterministic_proto_serialize` | Whether to use a deterministic serialization when calling SerializeToString(). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor) that represents the input items. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|-------------|--------------------------------------------------------------|\n| `TypeError` | In case of invalid arguments provided in `items_to_convert`. |\n\n\u003cbr /\u003e"]]
View source on GitHub