tfq.layers.State

A Layer that simulates a quantum state.

tfq.layers.State(
    backend=None, **kwargs
)

Used in the notebooks

Used in the tutorials
Research tools

Given an input circuit and set of parameter values, Simulate a quantum state and output it to the Tensorflow graph.

A more common application is for determining the set of states produced by a parametrized circuit where the values of the parameters vary. Suppose we want to generate a family of states with varying degrees of entanglement ranging from separable to maximally entangled. We first define a parametrized circuit that can accomplish this

q0, q1 = cirq.GridQubit.rect(1, 2)
alpha = sympy.Symbol('alpha') # degree of entanglement between q0, q1
parametrized_bell_circuit = cirq.Circuit(
   cirq.H(q0), cirq.CNOT(q0, q1) ** alpha)

Now pass all of the alpha values desired to tfq.layers.State to compute a tensor of states corresponding to these preparation angles.

state_layer = tfq.layers.State()
alphas = tf.reshape(tf.range(0, 1.1, delta=0.5), (3, 1)) # FIXME: #805
state_layer(parametrized_bell_circuit,
    symbol_names=[alpha], symbol_values=alphas)
<tf.RaggedTensor [[0.707106, 0j, 0.707106, 0j],
[(0.707106-1.2802768623032534e-08j), 0j,
    (0.353553+0.3535534143447876j), (0.353553-0.3535533547401428j)],
[(0.707106-1.2802768623032534e-08j), 0j,
    (0.-3.0908619663705394e-08j), (0.707106+6.181723932741079e-08j)]]>

This use case can be simplified to compute the state vector produced by a fixed circuit where the values of the parameters vary. For example, this layer produces a Bell state.

q0, q1 = cirq.GridQubit.rect(1, 2)
bell_circuit = cirq.Circuit(cirq.H(q0), cirq.CNOT(q0, q1))
state_layer = tfq.layers.State()
state_layer(bell_circuit)
<tf.RaggedTensor [[(0.707106-1.2802768623032534e-08j),
                    0j,
                   (0.-3.0908619663705394e-08j),
                   (0.707106+6.181723932741079e-08j)]]>

Not specifying symbol_names or symbol_values indicates that the circuit(s) does not contain any sympy.Symbols inside of it and tfq won't look for any symbols to resolve.

tfq.layers.State also allows for a more complicated input signature wherein a different (possibly parametrized) circuit is used to prepare a state for each batch of input parameters. This might be useful when the State layer is being used to generate entirely different families of states. Suppose we want to generate a stream of states that are either computational basis states or 'diagonal' basis states (as in the BB84 QKD protocol). The circuits to prepare these states are:

q0 = cirq.GridQubit(0, 0)
bitval = sympy.Symbol('bitval')
computational_circuit = cirq.Circuit(cirq.X(q0) ** bitval)
diagonal_circuit = cirq.Circuit(cirq.X(q0) ** bitval, cirq.H(q0))

Now a stream of random classical bit values can be encoded into one of these bases by preparing a state layer and passing in the bit values accompanied by their preparation circuits

qkd_layer = tfq.layers.State()
bits = [[1], [1], [0], [0]]
states_to_send = [computational_circuit,
                  diagonal_circuit,
                  diagonal_circuit,
                  computational_circuit]
qkd_states = qkd_layer(
    states_to_send, symbol_names=[bitval], symbol_values=bits)
# The third state was a '0' prepared in the diagonal basis:
qkd_states
<tf.RaggedTensor [[-4.371138828673793e-08j, (1+4.371138828673793e-08j)],
[(0.707106+3.0908619663705394e-08j), (-0.707106-1.364372508305678e-07j)],
[(0.707106-1.2802768623032534e-08j), (0.707106+3.0908619663705394e-08j)],
[(1+0j), 0j]]>

Args
`backend`	Optional Backend to use to simulate this state. Defaults to the native TensorFlow Quantum state vector simulator, however users may also specify a preconfigured cirq execution object to use instead, which must inherit `cirq.SimulatesFinalState`. Note that C++ Density Matrix simulation is not yet supported so to do Density Matrix simulation please use `cirq.DensityMatrixSimulator`.

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 `SeedGenerator`s.
`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`.
`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 `SeedGenerator`s. 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, *, symbol_names=None, symbol_values=None
)

Keras call function.

Input options
`inputs`, `symbol_names`, `symbol_values`: see `input_checks.expand_circuits`

Output shape
`tf.RaggedTensor` with shape: [batch size of symbol_values, ] or [number of circuits, ]

`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.

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)`.

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.

tfq.layers.State Stay organized with collections Save and categorize content based on your preferences.

Used in the notebooks

Args

Attributes

Methods

add_loss

Example:

add_metric

add_variable

add_weight

build

build_from_config

call

compute_mask

compute_output_shape

compute_output_spec

count_params

from_config

get_build_config

get_config

get_weights

load_own_variables

quantize

quantized_build

quantized_call

rematerialized_call

save_own_variables

set_weights

stateless_call

Example:

symbolic_call

__call__

tfq.layers.State

`add_loss`

`add_metric`

`add_variable`

`add_weight`

`build`

`build_from_config`

`call`

`compute_mask`

`compute_output_shape`

`compute_output_spec`

`count_params`

`from_config`

`get_build_config`

`get_config`

`get_weights`

`load_own_variables`

`quantize`

`quantized_build`

`quantized_call`

`rematerialized_call`

`save_own_variables`

`set_weights`

`stateless_call`

`symbolic_call`

`call`