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tfq.layers.PQC

View source on GitHub

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:

  • 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.

    NOTE: This is not the same as the self.name_scope.name which includes parent module names.

  • 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) == []
  • 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

__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).

build

View source

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(
    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:

  • 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.

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.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.