tfq.append_circuit
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Merge programs in the input tensors.
tfq.append_circuit(
programs, programs_to_append
)
Given two tensors of programs, this function merges the programs pairwise
and returns a single tensor containing the merged programs. Note that this
function is not differentiable because the output has type string.
q = cirq.GridQubit(0, 0)
p0 = [cirq.Circuit(cirq.H(q)), cirq.Circuit(cirq.S(q))]
p1 = [cirq.Circuit(cirq.Z(q)), cirq.Circuit(cirq.X(q))]
p0_t = tfq.convert_to_tensor(p0)
p1_t = tfq.convert_to_tensor(p1)
tfq.from_tensor(append_circuit(p0_t, p1_t))
array([cirq.Circuit([
cirq.Moment(
cirq.H(cirq.GridQubit(0, 0)),
),
cirq.Moment(
cirq.Z(cirq.GridQubit(0, 0)),
),
]),
cirq.Circuit([
cirq.Moment(
cirq.S(cirq.GridQubit(0, 0)),
),
cirq.Moment(
cirq.X(cirq.GridQubit(0, 0)),
),
])], dtype=object)
Args |
|---|
programs
|
tf.Tensor of strings with shape [batch_size] containing
the string representations of circuits.
|
programs_to_append
|
tf.Tensor of strings with shape [batch_size]
containing the string representations of circuits to append.
|
Returns |
|---|
tf.Tensor with shape [batch_size]. Entry i is the string
representing the circuit which is programs_to_append[i]
appended to programs[i].
|
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Last updated 2024-05-17 UTC.
[null,null,["Last updated 2024-05-17 UTC."],[],[],null,["# tfq.append_circuit\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/quantum/blob/v0.7.3/tensorflow_quantum/core/ops/tfq_utility_ops.py#L22-L65) |\n\nMerge programs in the input tensors. \n\n tfq.append_circuit(\n programs, programs_to_append\n )\n\nGiven two tensors of programs, this function merges the programs pairwise\nand returns a single tensor containing the merged programs. Note that this\nfunction is not differentiable because the output has type string. \n\n q = cirq.GridQubit(0, 0)\n p0 = [cirq.Circuit(cirq.H(q)), cirq.Circuit(cirq.S(q))]\n p1 = [cirq.Circuit(cirq.Z(q)), cirq.Circuit(cirq.X(q))]\n p0_t = tfq.convert_to_tensor(p0)\n p1_t = tfq.convert_to_tensor(p1)\n tfq.from_tensor(append_circuit(p0_t, p1_t))\n array([cirq.Circuit([\n cirq.Moment(\n cirq.H(cirq.GridQubit(0, 0)),\n ),\n cirq.Moment(\n cirq.Z(cirq.GridQubit(0, 0)),\n ),\n ]),\n cirq.Circuit([\n cirq.Moment(\n cirq.S(cirq.GridQubit(0, 0)),\n ),\n cirq.Moment(\n cirq.X(cirq.GridQubit(0, 0)),\n ),\n ])], dtype=object)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `programs` | [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor) of strings with shape \\[batch_size\\] containing the string representations of circuits. |\n| `programs_to_append` | [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor) of strings with shape \\[batch_size\\] containing the string representations of circuits to append. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor) with shape \\[batch_size\\]. Entry `i` is the string representing the circuit which is `programs_to_append[i]` appended to `programs[i]`. ||\n\n\u003cbr /\u003e"]]
View source on GitHub