tfq.layers.ControlledPQC

Controlled Parametrized Quantum Circuit (PQC) Layer.

tfq.layers.ControlledPQC(
    model_circuit,
    operators,
    *,
    repetitions=None,
    backend='noiseless',
    differentiator=None,
    **kwargs
)

Used in the notebooks

Used in the tutorials
Hello, many worlds Parametrized Quantum Circuits for Reinforcement Learning

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='noiseless' 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.

Attributes
compute_dtype	The dtype of the computations performed by the layer.
dtype	Alias of layer.variable_dtype.
dtype_policy
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
input_dtype	The dtype layer inputs should be converted to.
input_spec
losses	List of scalar losses from add_loss, regularizers and sublayers.
metrics	List of all metrics.
metrics_variables	List of all metric variables.
non_trainable_variables	List of all non-trainable layer state. This extends layer.non_trainable_weights to include all state used by the layer including state for metrics and SeedGenerators.
non_trainable_weights	List of all non-trainable weight variables of the layer. These are the weights that should not be updated by the optimizer during training. Unlike, layer.non_trainable_variables this excludes metric state and random seeds.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.
path	The path of the layer. If the layer has not been built yet, it will be None.
quantization_mode	The quantization mode of this layer, None if not quantized.
supports_masking	Whether this layer supports computing a mask using compute_mask.
symbols	The symbols that are managed by this layer (in-order). Note: symbols[i] indicates what symbol name the managed variables in this layer map to.
trainable	Settable boolean, whether this layer should be trainable or not.
trainable_variables	List of all trainable layer state. This is equivalent to layer.trainable_weights.
trainable_weights	List of all trainable weight variables of the layer. These are the weights that get updated by the optimizer during training.
variable_dtype	The dtype of the state (weights) of the layer.
variables	List of all layer state, including random seeds. This extends layer.weights to include all state used by the layer including SeedGenerators. Note that metrics variables are not included here, use metrics_variables to visit all the metric variables.
weights	List of all weight variables of the layer. Unlike, layer.variables this excludes metric state and random seeds.

Methods

add_loss

add_loss(
    loss
)

Can be called inside of the call() method to add a scalar loss.

Example:

class MyLayer(Layer):
    ...
    def call(self, x):
        self.add_loss(ops.sum(x))
        return x

add_metric

add_metric(
    *args, **kwargs
)

add_variable

add_variable(
    shape,
    initializer,
    dtype=None,
    trainable=True,
    autocast=True,
    regularizer=None,
    constraint=None,
    name=None
)

Add a weight variable to the layer.

Alias of add_weight().

add_weight

add_weight(
    shape=None,
    initializer=None,
    dtype=None,
    trainable=True,
    autocast=True,
    regularizer=None,
    constraint=None,
    aggregation='none',
    overwrite_with_gradient=False,
    name=None
)

Add a weight variable to the layer.

Args
shape	Shape tuple for the variable. Must be fully-defined (no None entries). Defaults to () (scalar) if unspecified.
initializer	Initializer object to use to populate the initial variable value, or string name of a built-in initializer (e.g. "random_normal"). If unspecified, defaults to "glorot_uniform" for floating-point variables and to "zeros" for all other types (e.g. int, bool).
dtype	Dtype of the variable to create, e.g. "float32". If unspecified, defaults to the layer's variable dtype (which itself defaults to "float32" if unspecified).
trainable	Boolean, whether the variable should be trainable via backprop or whether its updates are managed manually. Defaults to True.
autocast	Boolean, whether to autocast layers variables when accessing them. Defaults to True.
regularizer	Regularizer object to call to apply penalty on the weight. These penalties are summed into the loss function during optimization. Defaults to None.
constraint	Contrainst object to call on the variable after any optimizer update, or string name of a built-in constraint. Defaults to None.
aggregation	Optional string, one of None, "none", "mean", "sum" or "only_first_replica". Annotates the variable with the type of multi-replica aggregation to be used for this variable when writing custom data parallel training loops. Defaults to "none".
overwrite_with_gradient	Boolean, whether to overwrite the variable with the computed gradient. This is useful for float8 training. Defaults to False.
name	String name of the variable. Useful for debugging purposes.

build

build(
    input_shape
)

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.

call

View source

call(
    inputs
)

Keras call function.

compute_mask

compute_mask(
    inputs, previous_mask
)

compute_output_shape

compute_output_shape(
    *args, **kwargs
)

compute_output_spec

compute_output_spec(
    *args, **kwargs
)

count_params

count_params()

Count the total number of scalars composing the weights.

Returns
An integer count.

from_config

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config	A Python dictionary, typically the output of get_config.

Returns
An operation 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 object.

An object config is a Python dictionary (serializable) containing the information needed to re-instantiate it.

get_weights

get_weights()

Return the values of layer.weights 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.

quantize

quantize(
    mode, type_check=True
)

quantized_build

quantized_build(
    input_shape, mode
)

quantized_call

quantized_call(
    *args, **kwargs
)

rematerialized_call

rematerialized_call(
    layer_call, *args, **kwargs
)

Enable rematerialization dynamically for layer's call method.

Args
layer_call	The original call method of a layer.

Returns
Rematerialized layer's call method.

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 values of layer.weights from a list of NumPy arrays.

stateless_call

stateless_call(
    trainable_variables,
    non_trainable_variables,
    *args,
    return_losses=False,
    **kwargs
)

Call the layer without any side effects.

Args
trainable_variables	List of trainable variables of the model.
non_trainable_variables	List of non-trainable variables of the model.
*args	Positional arguments to be passed to call().
return_losses	If True, stateless_call() will return the list of losses created during call() as part of its return values.
**kwargs	Keyword arguments to be passed to call().

Returns
A tuple. By default, returns (outputs, non_trainable_variables). If return_losses = True, then returns (outputs, non_trainable_variables, losses).

Note: non_trainable_variables include not only non-trainable weights such as BatchNormalization statistics, but also RNG seed state (if there are any random operations part of the layer, such as dropout), and Metric state (if there are any metrics attached to the layer). These are all elements of state of the layer.

Example:

model = ...
data = ...
trainable_variables = model.trainable_variables
non_trainable_variables = model.non_trainable_variables
# Call the model with zero side effects
outputs, non_trainable_variables = model.stateless_call(
    trainable_variables,
    non_trainable_variables,
    data,
)
# Attach the updated state to the model
# (until you do this, the model is still in its pre-call state).
for ref_var, value in zip(
    model.non_trainable_variables, non_trainable_variables
):
    ref_var.assign(value)

symbolic_call

symbolic_call(
    *args, **kwargs
)

__call__

__call__(
    *args, **kwargs
)

Call self as a function.