tfq.layers.ControlledPQC

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Controlled Parametrized Quantum Circuit (PQC) Layer.

Used in the notebooks

Used in the tutorials

The ControlledPQC layer is very similar to the regular PQC layer, but with one major difference. The ControlledPQC layer requires the caller of the layer to provide the control parameter inputs for model_circuit. You can see how this works through a simple example:

bit = cirq.GridQubit(0, 0)
model = cirq.Circuit(
    cirq.X(bit) ** sympy.Symbol('alpha'),
    cirq.Z(bit) ** sympy.Symbol('beta')
)
outputs = tfq.layers.ControlledPQC(model, cirq.Z(bit))
quantum_data = tfq.convert_to_tensor([
    cirq.Circuit(),
    cirq.Circuit(cirq.X(bit))
])
model_params = tf.convert_to_tensor([[0.5, 0.5], [0.25, 0.75]])
res = outputs([quantum_data, model_params])
res
tf.Tensor(
[[-1.4901161e-08]
 [-7.0710683e-01]], shape=(2, 1), dtype=float32)

Just like with the PQC it is very important that the quantum datapoint circuits do not contain any sympy.Symbols themselves (This can be supported with advanced usage of the tfq.layers.Expectation layer). Just like PQC it is possible to specify multiple readout operations and switch to sample based expectation calculation:

bit = cirq.GridQubit(0, 0)
model = cirq.Circuit(
    cirq.X(bit) ** sympy.Symbol('alpha'),
    cirq.Z(bit) ** sympy.Symbol('beta')
)
outputs = tfq.layers.ControlledPQC(
    model,
    [cirq.Z(bit), cirq.X(bit), cirq.Y(bit)],
    repetitions=5000)
quantum_data = tfq.convert_to_tensor([
    cirq.Circuit(),
    cirq.Circuit(cirq.X(bit))
])
model_params = tf.convert_to_tensor([[0.5, 0.5], [0.25, 0.75]])
res = outputs([quantum_data, model_params])
res
tf.Tensor(
[[-0.0028  1.     -0.0028]
 [-0.6956 -0.498  -0.498 ]], shape=(2, 3), 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 ControlledPQC layer should use. Here's how you would take the gradients of the above example using a cirq.Simulator backend (which is slower than backend=None which uses C++):

bit = cirq.GridQubit(0, 0)
model = cirq.Circuit(
    cirq.X(bit) ** sympy.Symbol('alpha'),
    cirq.Z(bit) ** sympy.Symbol('beta')
)
outputs = tfq.layers.ControlledPQC(
    model,
    [cirq.Z(bit), cirq.X(bit), cirq.Y(bit)],
    repetitions=5000,
    backend=cirq.Simulator(),
    differentiator=tfq.differentiators.ParameterShift())
quantum_data = tfq.convert_to_tensor([
    cirq.Circuit(),
    cirq.Circuit(cirq.X(bit))
])
model_params = tf.convert_to_tensor([[0.5, 0.5], [0.25, 0.75]])
with tf.GradientTape() as g:
    g.watch(model_params)
    res = outputs([quantum_data, model_params])
grads = g.gradient(res, model_params)
grads
tf.Tensor(
[[-3.1415927   3.1415927 ]
 [-0.9211149   0.02764606]], shape=(2, 2), dtype=float32)]

Lastly, like all layers in TensorFlow the ControlledPQC layer can be called on any tf.Tensor as long as it is the right shape. This means you could replace model_params in the above example with the outputs from a tf.keras.Dense layer or replace quantum_data with values fed in from a tf.keras.Input.

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.

name_scope Returns a tf.name_scope instance for this class.
non_trainable_variables

non_trainable_weights

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.

output_mask Retrieves the output 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.

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

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

submodules Sequence of all sub-modules.

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) == []

symbols The symbols that are managed by this layer (in-order).

trainable

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

trainable_weights

updates

variables Returns the list of all layer variables/weights.

Alias of self.weights.

weights Returns the list of all layer variables/weights.

Methods

build

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Arguments
input_shape Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

compute_mask

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

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 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

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

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

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

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

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

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

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

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

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

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

Returns the current weights of the layer.

Returns
Weights values as a list of numpy arrays.

set_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.

with_name_scope

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.Variables and tf.Tensors whose names included the module name:

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

Args
method The method to wrap.

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

__call__

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).