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tfq.layers.SampledExpectation

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A layer that calculates a sampled expectation value.

tfq.layers.SampledExpectation(
    backend=None, differentiator=None, **kwargs
)

Given an input circuit and set of parameter values, output expectation values of observables computed using measurement results sampled from the input circuit.

First define a simple helper function for generating a parametrized quantum circuit that we will use throughout:

def _gen_single_bit_rotation_problem(bit, symbols): 
    """Generate a toy problem on 1 qubit.""" 
    starting_state = [0.123, 0.456, 0.789] 
    circuit = cirq.Circuit( 
        cirq.Rx(starting_state[0])(bit), 
        cirq.Ry(starting_state[1])(bit), 
        cirq.Rz(starting_state[2])(bit), 
        cirq.Rz(symbols[2])(bit), 
        cirq.Ry(symbols[1])(bit), 
        cirq.Rx(symbols[0])(bit) 
    ) 
    return circuit 

In quantum machine learning there are two very common use cases that align with keras layer constructs. The first is where the circuits represent the input data points:

bit = cirq.GridQubit(0, 0) 
symbols = sympy.symbols('x y z') 
ops = [-1.0 * cirq.Z(bit), cirq.X(bit) + 2.0 * cirq.Z(bit)] 
num_samples = [100, 200] 
circuit_list = [ 
    _gen_single_bit_rotation_problem(bit, symbols), 
    cirq.Circuit( 
        cirq.Z(bit) ** symbols[0], 
        cirq.X(bit) ** symbols[1], 
        cirq.Z(bit) ** symbols[2] 
    ), 
    cirq.Circuit( 
        cirq.X(bit) ** symbols[0], 
        cirq.Z(bit) ** symbols[1], 
        cirq.X(bit) ** symbols[2] 
    ) 
] 
sampled_expectation_layer = tfq.layers.SampledExpectation() 
output = sampled_expectation_layer( 
    circuit_list, 
    symbol_names=symbols, 
    operators=ops, 
    repetitions=num_samples) 
# Here output[i][j] corresponds to the sampled expectation 
# of all the ops in ops w.r.t circuits[i] where Keras managed 
# variables are placed in the symbols 'x', 'y', 'z'. 
tf.shape(output) 
tf.Tensor([3 2], shape=(2,), dtype=int32) 

Here, different cirq.Circuit instances sharing the common symbols 'x', 'y' and 'z' are used as input. Keras uses the symbol_names argument to map Keras managed variables to these circuits constructed with sympy.Symbols. The shape of num_samples is equal to that of ops.

The second most common use case is where there is a fixed circuit and the expectation operators vary:

bit = cirq.GridQubit(0, 0) 
symbols = sympy.symbols('x, y, z') 
ops = [-1.0 * cirq.Z(bit), cirq.X(bit) + 2.0 * cirq.Z(bit)] 
fixed_circuit = _gen_single_bit_rotation_problem(bit, symbols) 
expectation_layer = tfq.layers.SampledExpectation() 
output = expectation_layer( 
    fixed_circuit, 
    symbol_names=symbols, 
    operators=ops, 
    repetitions=5000, 
    initializer=tf.keras.initializers.RandomUniform(0, 2 * np.pi)) 
# Here output[i][j] corresponds to 
# the sampled expectation of operators[i][j] using 5000 samples w.r.t 
# the circuit where variable values are managed by keras and store 
# numbers in the symbols 'x', 'y', 'z'. 
tf.shape(output) 
tf.Tensor([1 2], shape=(2,), dtype=int32) 

Here different cirq.PauliSum or cirq.PauliString instances can be used as input to calculate the expectation on the fixed circuit that the layer was initially constructed with.

There are also some more complex use cases that provide greater flexibility. Notably these configurations all make use of the symbol_values parameter that causes the SampledExpectation layer to stop managing the sympy.Symbols in the quantum circuits and instead requires the user to supply inputs themselves. Lets look at the case where there is a single fixed circuit, some fixed operators and symbols that must be common to all circuits:

bit = cirq.GridQubit(0, 0) 
symbols = sympy.symbols('x y z') 
ops = [cirq.Z(bit), cirq.X(bit)] 
num_samples = [100, 200] 
circuit = _gen_single_bit_rotation_problem(bit, symbols) 
values = [[1,1,1], [2,2,2], [3,3,3]] 
sampled_expectation_layer = tfq.layers.SampledExpectation() 
output = sampled_expectation_layer( 
    circuit, 
    symbol_names=symbols, 
    symbol_values=values, 
    operators=ops, 
    repetitions=num_samples) 
# output[i][j] = The sampled expectation of ops[j] with 
# values_tensor[i] placed into the symbols of the circuit 
# with the order specified by feed_in_params. 
# so output[1][2] = The sampled expectation of a circuit with parameter 
# values [2,2,2] w.r.t Pauli X, estimated using 200 samples per term. 
output  # Non-deterministic result. It can vary every time. 
tf.Tensor( 
[[0.52, 0.72], 
 [0.34, 1.  ], 
 [0.78, 0.48]], shape=(3, 2), dtype=float32) 

Tip: you can compare the above result with that of Expectation: tf.Tensor( [[0.63005245 0.76338404] [0.25707167 0.9632684 ] [0.79086655 0.5441111 ]], shape=(3, 2), dtype=float32)

Here is a simple model that uses this particular input signature of tfq.layers.SampledExpectation, that learns to undo the random rotation of the qubit:

bit = cirq.GridQubit(0, 0) 
symbols = sympy.symbols('x, y, z') 
circuit = _gen_single_bit_rotation_problem(bit, symbols) 
control_input = tf.keras.Input(shape=(1,)) 
circuit_inputs = tf.keras.Input(shape=(), dtype=tf.dtypes.string) 
d1 = tf.keras.layers.Dense(10)(control_input) 
d2 = tf.keras.layers.Dense(3)(d1) 
expectation = tfq.layers.SampledExpectation()( 
    circuit_inputs, # See note below! 
    symbol_names=symbols, 
    symbol_values=d2, 
    operators=cirq.Z(bit), 
    repetitions=5000) 
data_in = np.array([[1], [0]], dtype=np.float32) 
data_out = np.array([[1], [-1]], dtype=np.float32) 
model = tf.keras.Model( 
    inputs=[circuit_inputs, control_input], outputs=expectation) 
model.compile( 
    optimizer=tf.keras.optimizers.Adam(learning_rate=0.01), 
    loss=tf.keras.losses.mean_squared_error) 
history = model.fit( 
    x=[tfq.convert_to_tensor([circuit] * 2), data_in], 
    y=data_out, 
    epochs=100) 

For an example featuring this layer, please check out Taking gradients in our dev website http://www.tensorflow.org/quantum/tutorials.

Lastly symbol_values, operators and circuit inputs can all be fed Python list objects. In addition to this they can also be fed tf.Tensor inputs, meaning that you can input all of these things from other Tensor objects (like tf.keras.Dense layer outputs or tf.keras.Inputs etc).

Args:

  • backend: Optional Backend to use to simulate states. Defaults to the native TensorFlow simulator (None), however users may also specify a preconfigured cirq simulation object to use instead, which must inherit cirq.SimulatesFinalState.
  • differentiator: Optional Differentiator to use to calculate analytic derivative values of given operators_to_measure and circuit, which must inherit tfq.differentiators.Differentiator. Defaults to None, which uses parameter_shift.ParameterShift().

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

build(
    input_shape
)

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

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.