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Parametrized Quantum Circuit (PQC) Layer.

```
tfq.layers.PQC(
model_circuit, operators, **kwargs
)
```

This layer is for training parameterized quantum models. Given a parameterized circuit, this layer initializes the parameters and manages them in a Keras native way.

We start by defining a simple quantum circuit on one qubit. This circuit parameterizes an arbitrary rotation on the Bloch sphere in terms of the three angles a, b, and c:

`q = cirq.GridQubit(0, 0)`

`(a, b, c) = sympy.symbols("a b c")`

`circuit = cirq.Circuit(`

`cirq.Rz(a)(q),`

`cirq.Rx(b)(q),`

`cirq.Rz(c)(q),`

`cirq.Rx(-b)(q),`

`cirq.Rz(-a)(q)`

`)`

In order to extract information from our circuit, we must apply measurement
operators. For now we choose to make a Z measurement. In order to observe
an output, we must also feed our model quantum data (NOTE: quantum data
means quantum circuits with no free parameters). Though the output values
will depend on the default random initialization of the angles in our model,
one will be the negative of the other since `cirq.X(q)`

causes a bit flip:

`outputs = tfq.layers.PQC(circuit, cirq.Z(q))`

`quantum_data = tfq.convert_to_tensor([`

`cirq.Circuit(),`

`cirq.Circuit(cirq.X(q))`

`])`

`res = outputs(quantum_data)`

`res`

`<tf.Tensor: id=577, shape=(2, 1), dtype=float32, numpy=`

`array([[ 0.8722095],`

`[-0.8722095]], dtype=float32)>`

We can also choose to measure the three pauli matrices, sufficient to fully characterize the operation of our model, or choose to simulate sampled expectation values by specifying a number of measurement shots (repetitions) to average over. Notice that using only 200 repetitions introduces variation between the two rows of data, due to the probabilistic nature of measurement.

`measurement = [cirq.X(q), cirq.Y(q), cirq.Z(q)]`

`outputs = tfq.layers.PQC(circuit, measurement, repetitions=200)`

`quantum_data = tfq.convert_to_tensor([`

`cirq.Circuit(),`

`cirq.Circuit(cirq.X(q))`

`])`

`res = outputs(quantum_data)`

`res`

`<tf.Tensor: id=808, shape=(2, 3), dtype=float32, numpy=`

`array([[-0.38, 0.9 , 0.14],`

`[ 0.19, -0.95, -0.35]], dtype=float32)>`

A value for `backend`

can also be supplied in the layer constructor
arguments to indicate which supported backend you would like to use.
A value for `differentiator`

can also be supplied in the constructor
to indicate the differentiation scheme this `PQC`

layer should use.
Here's how you would take the gradients of the above example using a
`cirq.Simulator`

backend (which is slower than the default
`backend=None`

which uses C++):

`q = cirq.GridQubit(0, 0)`

`(a, b, c) = sympy.symbols("a b c")`

`circuit = cirq.Circuit(`

`cirq.Rz(a)(q),`

`cirq.Rx(b)(q),`

`cirq.Rz(c)(q),`

`cirq.Rx(-b)(q),`

`cirq.Rz(-a)(q)`

`)`

`measurement = [cirq.X(q), cirq.Y(q), cirq.Z(q)]`

`outputs = tfq.layers.PQC(`

`circuit,`

`measurement,`

`repetitions=5000,`

`backend=cirq.Simulator(),`

`differentiator=tfq.differentiators.ParameterShift())`

`quantum_data = tfq.convert_to_tensor([`

`cirq.Circuit(),`

`cirq.Circuit(cirq.X(q))`

`])`

`res = outputs(quantum_data)`

`res`

`<tf.Tensor: id=891, shape=(2, 3), dtype=float32, numpy=`

`array([[-0.5956, -0.2152, 0.7756],`

`[ 0.5728, 0.1944, -0.7848]], dtype=float32)>`

Lastly, like all layers in TensorFlow the `PQC`

layer can be called on any
`tf.Tensor`

as long as it is the right shape. This means you could replace
replace `quantum_data`

with values fed in from a `tf.keras.Input`

.

#### Attributes:

: Optional regularizer function for the output of this layer.`activity_regularizer`

`dtype`

`dynamic`

: Retrieves the input tensor(s) of a layer.`input`

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

: Retrieves the input mask tensor(s) of a layer.`input_mask`

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

: Retrieves the input shape(s) of a layer.`input_shape`

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

`input_spec`

: Losses which are associated with this`losses`

`Layer`

.Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing

`losses`

under a`tf.GradientTape`

will propagate gradients back to the corresponding variables.`metrics`

: Returns the name of this module as passed or determined in the ctor.`name`

NOTE: This is not the same as the

`self.name_scope.name`

which includes parent module names.: Returns a`name_scope`

`tf.name_scope`

instance for this class.`non_trainable_variables`

`non_trainable_weights`

: Retrieves the output tensor(s) of a layer.`output`

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

: Retrieves the output mask tensor(s) of a layer.`output_mask`

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

: Retrieves the output shape(s) of a layer.`output_shape`

Only applicable if the layer has one output, or if all outputs have the same shape.

: Sequence of all sub-modules.`submodules`

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

`a = tf.Module() b = tf.Module() c = tf.Module() a.b = b b.c = c assert list(a.submodules) == [b, c] assert list(b.submodules) == [c] assert list(c.submodules) == []`

`trainable`

: Sequence of trainable variables owned by this module and its submodules.`trainable_variables`

`trainable_weights`

`updates`

: Returns the list of all layer variables/weights.`variables`

Alias of

`self.weights`

.: Returns the list of all layer variables/weights.`weights`

## Methods

`__call__`

```
__call__(
inputs, *args, **kwargs
)
```

Wraps `call`

, applying pre- and post-processing steps.

#### Arguments:

: input tensor(s).`inputs`

: additional positional arguments to be passed to`*args`

`self.call`

.: additional keyword arguments to be passed to`**kwargs`

`self.call`

.

#### Returns:

Output tensor(s).

#### Note:

- The following optional keyword arguments are reserved for specific uses:
`training`

: Boolean scalar tensor of Python boolean indicating whether the`call`

is meant for training or inference.`mask`

: Boolean input mask.

- If the layer's
`call`

method takes a`mask`

argument (as some Keras layers do), its default value will be set to the mask generated for`inputs`

by the previous layer (if`input`

did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support.

#### Raises:

: if the layer's`ValueError`

`call`

method returns None (an invalid value).

`build`

```
build(
input_shape
)
```

Keras build function.

`compute_mask`

```
compute_mask(
inputs, mask=None
)
```

Computes an output mask tensor.

#### Arguments:

: Tensor or list of tensors.`inputs`

: Tensor or list of tensors.`mask`

#### Returns:

None or a tensor (or list of tensors, one per output tensor of the layer).

`compute_output_shape`

```
compute_output_shape(
input_shape
)
```

Computes the output shape of the layer.

If the layer has not been built, this method will call `build`

on the
layer. This assumes that the layer will later be used with inputs that
match the input shape provided here.

#### Arguments:

: Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.`input_shape`

#### Returns:

An input shape tuple.

`count_params`

```
count_params()
```

Count the total number of scalars composing the weights.

#### Returns:

An integer count.

#### Raises:

: if the layer isn't yet built (in which case its weights aren't yet defined).`ValueError`

`from_config`

```
@classmethod
from_config(
cls, config
)
```

Creates a layer from its config.

This method is the reverse of `get_config`

,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by `set_weights`

).

#### Arguments:

: A Python dictionary, typically the output of get_config.`config`

#### Returns:

A layer instance.

`get_config`

```
get_config()
```

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by `Network`

(one layer of abstraction above).

#### Returns:

Python dictionary.

`get_input_at`

```
get_input_at(
node_index
)
```

Retrieves the input tensor(s) of a layer at a given node.

#### Arguments:

: Integer, index of the node from which to retrieve the attribute. E.g.`node_index`

`node_index=0`

will correspond to the first time the layer was called.

#### Returns:

A tensor (or list of tensors if the layer has multiple inputs).

#### Raises:

: If called in Eager mode.`RuntimeError`

`get_input_mask_at`

```
get_input_mask_at(
node_index
)
```

Retrieves the input mask tensor(s) of a layer at a given node.

#### Arguments:

: Integer, index of the node from which to retrieve the attribute. E.g.`node_index`

`node_index=0`

will correspond to the first time the layer was called.

#### Returns:

A mask tensor (or list of tensors if the layer has multiple inputs).

`get_input_shape_at`

```
get_input_shape_at(
node_index
)
```

Retrieves the input shape(s) of a layer at a given node.

#### Arguments:

: Integer, index of the node from which to retrieve the attribute. E.g.`node_index`

`node_index=0`

will correspond to the first time the layer was called.

#### Returns:

A shape tuple (or list of shape tuples if the layer has multiple inputs).

#### Raises:

: If called in Eager mode.`RuntimeError`

`get_losses_for`

```
get_losses_for(
inputs
)
```

Retrieves losses relevant to a specific set of inputs.

#### Arguments:

: Input tensor or list/tuple of input tensors.`inputs`

#### Returns:

List of loss tensors of the layer that depend on `inputs`

.

`get_output_at`

```
get_output_at(
node_index
)
```

Retrieves the output tensor(s) of a layer at a given node.

#### Arguments:

: Integer, index of the node from which to retrieve the attribute. E.g.`node_index`

`node_index=0`

will correspond to the first time the layer was called.

#### Returns:

A tensor (or list of tensors if the layer has multiple outputs).

#### Raises:

: If called in Eager mode.`RuntimeError`

`get_output_mask_at`

```
get_output_mask_at(
node_index
)
```

Retrieves the output mask tensor(s) of a layer at a given node.

#### Arguments:

: Integer, index of the node from which to retrieve the attribute. E.g.`node_index`

`node_index=0`

will correspond to the first time the layer was called.

#### Returns:

A mask tensor (or list of tensors if the layer has multiple outputs).

`get_output_shape_at`

```
get_output_shape_at(
node_index
)
```

Retrieves the output shape(s) of a layer at a given node.

#### Arguments:

: Integer, index of the node from which to retrieve the attribute. E.g.`node_index`

`node_index=0`

will correspond to the first time the layer was called.

#### Returns:

A shape tuple (or list of shape tuples if the layer has multiple outputs).

#### Raises:

: If called in Eager mode.`RuntimeError`

`get_updates_for`

```
get_updates_for(
inputs
)
```

Retrieves updates relevant to a specific set of inputs.

#### Arguments:

: Input tensor or list/tuple of input tensors.`inputs`

#### Returns:

List of update ops of the layer that depend on `inputs`

.

`get_weights`

```
get_weights()
```

Returns the current weights of the layer.

#### Returns:

Weights values as a list of numpy arrays.

`set_weights`

```
set_weights(
weights
)
```

Sets the weights of the layer, from Numpy arrays.

#### Arguments:

: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of`weights`

`get_weights`

).

#### Raises:

: If the provided weights list does not match the layer's specifications.`ValueError`

`with_name_scope`

```
@classmethod
with_name_scope(
cls, method
)
```

Decorator to automatically enter the module name scope.

```
class MyModule(tf.Module):
@tf.Module.with_name_scope
def __call__(self, x):
if not hasattr(self, 'w'):
self.w = tf.Variable(tf.random.normal([x.shape[1], 64]))
return tf.matmul(x, self.w)
```

Using the above module would produce `tf.Variable`

s and `tf.Tensor`

s whose
names included the module name:

```
mod = MyModule()
mod(tf.ones([8, 32]))
# ==> <tf.Tensor: ...>
mod.w
# ==> <tf.Variable ...'my_module/w:0'>
```

#### Args:

: The method to wrap.`method`

#### Returns:

The original method wrapped such that it enters the module's name scope.