tfq.resolve_parameters
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Replace symbols in a batch of programs with concrete values.
tfq.resolve_parameters(
programs, symbol_names, symbol_values
)
Used in the notebooks
This function has the ability to partially resolve parameters, so that
symbol_names can contain fewer symbols than programs; symbols not listed
remain unresolved in the output programs. Note also that because the output
of this function has type string, the function is not differentiable.
qubit = cirq.GridQubit(0, 0)
symbol = sympy.Symbol('alpha')
my_c = cirq.Circuit(cirq.H(qubit) ** symbol)
tensor_c = tfq.convert_to_tensor([my_c])
tfq.from_tensor(tfq.resolve_parameters(tensor_c, ['alpha'], [[0.2]]))
[(0, 0): ───H^0.2───]
Args |
|---|
programs
|
tf.Tensor of strings with shape [batch_size] containing
the string representations of the circuits to be resolved.
|
symbol_names
|
tf.Tensor of strings with shape [n_params], which
is used to specify the order in which the values in
symbol_values should be placed inside of the circuits in
programs.
|
symbol_values
|
tf.Tensor of real numbers with shape
[batch_size, n_params] specifying parameter values to resolve
into the circuits specified by programs, following the ordering
dictated by symbol_names.
|
Returns |
|---|
tf.Tensor with shape [batch_size]. Each entry corresponds to the
original circuit in program except with symbols listed in
symbol_names replaced with their corresponding values.
|
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Last updated 2024-05-17 UTC.
[null,null,["Last updated 2024-05-17 UTC."],[],[],null,["# tfq.resolve_parameters\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#L106-L141) |\n\nReplace symbols in a batch of programs with concrete values. \n\n tfq.resolve_parameters(\n programs, symbol_names, symbol_values\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|-----------------------------------------------------------------------------|\n| - [Quantum data](https://www.tensorflow.org/quantum/tutorials/quantum_data) |\n\nThis function has the ability to partially resolve parameters, so that\n`symbol_names` can contain fewer symbols than `programs`; symbols not listed\nremain unresolved in the output programs. Note also that because the output\nof this function has type string, the function is not differentiable. \n\n qubit = cirq.GridQubit(0, 0)\n symbol = sympy.Symbol('alpha')\n my_c = cirq.Circuit(cirq.H(qubit) ** symbol)\n tensor_c = tfq.convert_to_tensor([my_c])\n tfq.from_tensor(tfq.resolve_parameters(tensor_c, ['alpha'], [[0.2]]))\n [(0, 0): ───H^0.2───]\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 the circuits to be resolved. |\n| `symbol_names` | [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor) of strings with shape \\[n_params\\], which is used to specify the order in which the values in `symbol_values` should be placed inside of the circuits in `programs`. |\n| `symbol_values` | [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor) of real numbers with shape \\[batch_size, n_params\\] specifying parameter values to resolve into the circuits specified by programs, following the ordering dictated by `symbol_names`. |\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\\]. Each entry corresponds to the original circuit in `program` except with symbols listed in `symbol_names` replaced with their corresponding values. ||\n\n\u003cbr /\u003e"]]
View source on GitHub