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Parametrized Quantum Circuit (PQC) Layer.
tfq.layers.PQC(
model_circuit, operators, *, repetitions=None, backend=None,
differentiator=None, initializer=tf.keras.initializers.RandomUniform(0, 2 *
np.pi), regularizer=None, constraint=None, **kwargs
)
Used in the notebooks
Used in the tutorials 

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  

activity_regularizer

Optional regularizer function for the output of this layer. 
dtype

Dtype used by the weights of the layer, set in the constructor. 
dynamic

Whether the layer is dynamic (eageronly); set in the constructor. 
input

Retrieves the input tensor(s) of a layer.
Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer. 
input_spec

InputSpec instance(s) describing the input format for this layer.
When you create a layer subclass, you can set
Now, if you try to call the layer on an input that isn't rank 4
(for instance, an input of shape
Input checks that can be specified via
For more information, see 
losses

Losses which are associated with this Layer .
Variable regularization tensors are created when this property is accessed,
so it is eager safe: accessing 
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor. 
name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all nontrainable weights tracked by this layer.
Nontrainable weights are not updated during training. They are expected
to be updated manually in 
output

Retrieves the output tensor(s) of a layer.
Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer. 
submodules

Sequence of all 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).

symbols

The symbols that are managed by this layer (inorder). 
trainable


trainable_weights

List of all trainable weights tracked by this layer.
Trainable weights are updated via gradient descent during training. 
weights

Returns the list of all layer variables/weights. 
Methods
add_loss
add_loss(
losses, inputs=None
)
Add loss tensor(s), potentially dependent on layer inputs.
Some losses (for instance, activity regularization losses) may be dependent
on the inputs passed when calling a layer. Hence, when reusing the same
layer on different inputs a
and b
, some entries in layer.losses
may
be dependent on a
and some on b
. This method automatically keeps track
of dependencies.
This method can be used inside a subclassed layer or model's call
function, in which case losses
should be a Tensor or list of Tensors.
Example:
class MyLayer(tf.keras.layers.Layer):
def call(inputs, self):
self.add_loss(tf.abs(tf.reduce_mean(inputs)), inputs=True)
return inputs
This method can also be called directly on a Functional Model during
construction. In this case, any loss Tensors passed to this Model must
be symbolic and be able to be traced back to the model's Input
s. These
losses become part of the model's topology and are tracked in get_config
.
Example:
inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Activity regularization.
model.add_loss(tf.abs(tf.reduce_mean(x)))
If this is not the case for your loss (if, for example, your loss references
a Variable
of one of the model's layers), you can wrap your loss in a
zeroargument lambda. These losses are not tracked as part of the model's
topology since they can't be serialized.
Example:
inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(x.kernel))
The get_losses_for
method allows to retrieve the losses relevant to a
specific set of inputs.
Arguments  

losses

Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zeroargument callables which create a loss tensor. 
inputs

Ignored when executing eagerly. If anything other than None is
passed, it signals the losses are conditional on some of the layer's
inputs, and thus they should only be run where these inputs are
available. This is the case for activity regularization losses, for
instance. If None is passed, the losses are assumed
to be unconditional, and will apply across all dataflows of the layer
(e.g. weight regularization losses).

add_metric
add_metric(
value, aggregation=None, name=None
)
Adds metric tensor to the layer.
Args  

value

Metric tensor. 
aggregation

Samplewise metric reduction function. If aggregation=None ,
it indicates that the metric tensor provided has been aggregated
already. eg, bin_acc = BinaryAccuracy(name='acc') followed by
model.add_metric(bin_acc(y_true, y_pred)) . If aggregation='mean', the
given metric tensor will be samplewise reduced using mean function.
eg, model.add_metric(tf.reduce_sum(outputs), name='output_mean',
aggregation='mean') .

name

String metric name. 
Raises  

ValueError

If aggregation is anything other than None or mean .

build
build(
input_shape
)
Keras build function.
compute_mask
compute_mask(
inputs, mask=None
)
Computes an output mask tensor.
Arguments  

inputs

Tensor or list of tensors. 
mask

Tensor or list of tensors. 
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  

input_shape

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. 
Returns  

An input shape tuple. 
count_params
count_params()
Count the total number of scalars composing the weights.
Returns  

An integer count. 
Raises  

ValueError

if the layer isn't yet built (in which case its weights aren't yet defined). 
from_config
@classmethod
from_config( 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  

config

A Python dictionary, typically the output of get_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_weights
get_weights()
Returns the current weights of the layer.
The weights of a layer represent the state of the layer. This function returns both trainable and nontrainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.
For example, a Dense layer returns a list of two values peroutput weights and the bias value. These can be used to set the weights of another Dense layer:
a = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(1.))
a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
b = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(2.))
b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
b.set_weights(a.get_weights())
b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
Returns  

Weights values as a list of numpy arrays. 
set_weights
set_weights(
weights
)
Sets the weights of the layer, from Numpy arrays.
The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer's weights must be instantiated before calling this function by calling the layer.
For example, a Dense layer returns a list of two values peroutput weights and the bias value. These can be used to set the weights of another Dense layer:
a = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(1.))
a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
b = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(2.))
b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
b.set_weights(a.get_weights())
b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
Arguments  

weights

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 get_weights ).

Raises  

ValueError

If the provided weights list does not match the layer's specifications. 
symbol_values
symbol_values()
Returns a Python dict
containing symbol name, value pairs.
Returns  

Python dict with str keys and float values representing
the current symbol values.

with_name_scope
@classmethod
with_name_scope( 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], 3]))
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([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>
Args  

method

The method to wrap. 
Returns  

The original method wrapped such that it enters the module's name scope. 
__call__
__call__(
*args, **kwargs
)
Wraps call
, applying pre and postprocessing steps.
Arguments  

*args

Positional arguments to be passed to self.call .

**kwargs

Keyword arguments to be passed to 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 thecall
is meant for training or inference.mask
: Boolean input mask.
 If the layer's
call
method takes amask
argument (as some Keras layers do), its default value will be set to the mask generated forinputs
by the previous layer (ifinput
did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support.
Raises  

ValueError

if the layer's call method returns None (an invalid value).

RuntimeError

if super().__init__() was not called in the constructor.
