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A layer that calculates a sampled expectation value.
tfq.layers.SampledExpectation(
backend='noiseless', differentiator=None, **kwargs
)
Used in the notebooks
Used in the tutorials 

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.Symbol
s. 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.Symbol
s 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 # Nondeterministic result. It can vary every time.
tf.Tensor(
[[0.52, 0.72],
[0.34, 1. ],
[0.78, 0.48]], 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.Input
s etc).
Args  

backend

Optional Backend to use to simulate states. Can be either
{'noiseless', 'noisy'} users may also
specify a preconfigured cirq.Sampler object to use instead.

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 parameter_shift.ParameterShift() (None argument).

Attributes  

activity_regularizer

Optional regularizer function for the output of this layer. 
compute_dtype

The dtype of the layer's computations.
This is equivalent to Layers automatically cast their inputs to the compute dtype, which
causes computations and the output to be in the compute dtype as well.
This is done by the base Layer class in Layers often perform certain internal computations in higher precision
when 
dtype

The dtype of the layer weights.
This is equivalent to 
dtype_policy

The dtype policy associated with this layer.
This is an instance of a 
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

List of losses added using the add_loss() API.
Variable regularization tensors are created when this property is
accessed, so it is eager safe: accessing

metrics

List of metrics attached to 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).

supports_masking

Whether this layer supports computing a mask using compute_mask .

trainable


trainable_weights

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

Alias of Layer.dtype , the dtype of the weights.

weights

Returns the list of all layer variables/weights. 
Methods
add_loss
add_loss(
losses, **kwargs
)
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(self, inputs):
self.add_loss(tf.abs(tf.reduce_mean(inputs)))
return inputs
The same code works in distributed training: the input to add_loss()
is treated like a regularization loss and averaged across replicas
by the training loop (both builtin Model.fit()
and compliant custom
training loops).
The add_loss
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,))
d = tf.keras.layers.Dense(10)
x = d(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(d.kernel))
Args  

losses

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

Used for backwards compatibility only. 
build
build(
input_shape
)
Creates the variables of the layer (for subclass implementers).
This is a method that implementers of subclasses of Layer
or Model
can override if they need a statecreation step inbetween
layer instantiation and layer call. It is invoked automatically before
the first execution of call()
.
This is typically used to create the weights of Layer
subclasses
(at the discretion of the subclass implementer).
Args  

input_shape

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

build_from_config
build_from_config(
config
)
Builds the layer's states with the supplied config dict.
By default, this method calls the build(config["input_shape"])
method,
which creates weights based on the layer's input shape in the supplied
config. If your config contains other information needed to load the
layer's state, you should override this method.
Args  

config

Dict containing the input shape associated with this layer. 
compute_mask
compute_mask(
inputs, mask=None
)
Computes an output mask tensor.
Args  

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.
This method will cause the layer's state to be built, if that has not happened before. This requires that the layer will later be used with inputs that match the input shape provided here.
Args  

input_shape

Shape tuple (tuple of integers) or tf.TensorShape ,
or structure of shape tuples / tf.TensorShape instances
(one per output tensor of the layer).
Shape tuples can include None for free dimensions,
instead of an integer.

Returns  

A tf.TensorShape instance
or structure of tf.TensorShape instances.

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
).
Args  

config

A Python dictionary, typically the output of get_config. 
Returns  

A layer instance. 
get_build_config
get_build_config()
Returns a dictionary with the layer's input shape.
This method returns a config dict that can be used by
build_from_config(config)
to create all states (e.g. Variables and
Lookup tables) needed by the layer.
By default, the config only contains the input shape that the layer was built with. If you're writing a custom layer that creates state in an unusual way, you should override this method to make sure this state is already created when Keras attempts to load its value upon model loading.
Returns  

A dict containing the input shape associated with the layer. 
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).
Note that get_config()
does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.
Returns  

Python dictionary. 
get_weights
get_weights()
Returns the current weights of the layer, as NumPy arrays.
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: the kernel
matrix and the bias vector. These can be used to set the weights of
another Dense
layer:
layer_a = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(1.))
a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]]))
layer_a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
layer_b = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(2.))
b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]]))
layer_b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
layer_b.set_weights(layer_a.get_weights())
layer_b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
Returns  

Weights values as a list of NumPy arrays. 
load_own_variables
load_own_variables(
store
)
Loads the state of the layer.
You can override this method to take full control of how the state of
the layer is loaded upon calling keras.models.load_model()
.
Args  

store

Dict from which the state of the model will be loaded. 
save_own_variables
save_own_variables(
store
)
Saves the state of the layer.
You can override this method to take full control of how the state of
the layer is saved upon calling model.save()
.
Args  

store

Dict where the state of the model will be saved. 
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: the kernel
matrix and the bias vector. These can be used to set the weights of
another Dense
layer:
layer_a = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(1.))
a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]]))
layer_a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
layer_b = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(2.))
b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]]))
layer_b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
layer_b.set_weights(layer_a.get_weights())
layer_b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
Args  

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( 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.
Args  

*args

Positional arguments to be passed to self.call .

**kwargs

Keyword arguments to be passed to self.call .

Returns  

Output tensor(s). 
Note  


Raises  

ValueError

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

RuntimeError

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