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:
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.
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++):
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
dynamic
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_mask
Retrieves the input mask tensor(s) of a layer.
Only applicable if the layer has exactly one inbound node,
i.e. if it is connected to one incoming layer.
input_shape
Retrieves the input shape(s) of a layer.
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
Losses which are associated with this 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
name
Returns the name of this module as passed or determined in the ctor.
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
@classmethodfrom_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_input_at
get_input_at(
node_index
)
Retrieves the input tensor(s) of a layer at a given node.
Arguments
node_index
Integer, index of the node
from which to retrieve the attribute.
E.g. 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
RuntimeError
If called in Eager mode.
get_input_mask_at
get_input_mask_at(
node_index
)
Retrieves the input mask tensor(s) of a layer at a given node.
Arguments
node_index
Integer, index of the node
from which to retrieve the attribute.
E.g. 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
node_index
Integer, index of the node
from which to retrieve the attribute.
E.g. 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
RuntimeError
If called in Eager mode.
get_losses_for
get_losses_for(
inputs
)
Retrieves losses relevant to a specific set of inputs.
Arguments
inputs
Input tensor or list/tuple of input tensors.
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
node_index
Integer, index of the node
from which to retrieve the attribute.
E.g. 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
RuntimeError
If called in Eager mode.
get_output_mask_at
get_output_mask_at(
node_index
)
Retrieves the output mask tensor(s) of a layer at a given node.
Arguments
node_index
Integer, index of the node
from which to retrieve the attribute.
E.g. 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
node_index
Integer, index of the node
from which to retrieve the attribute.
E.g. 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
RuntimeError
If called in Eager mode.
get_updates_for
get_updates_for(
inputs
)
Retrieves updates relevant to a specific set of inputs.
Arguments
inputs
Input tensor or list/tuple of input tensors.
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
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.
The original method wrapped such that it enters the module's name scope.
__call__
__call__(
inputs, *args, **kwargs
)
Wraps call, applying pre- and post-processing steps.
Arguments
inputs
input tensor(s).
*args
additional positional arguments to be passed to self.call.
**kwargs
additional 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 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
ValueError
if the layer's call method returns None (an invalid value).