# tfq.layers.State

A Layer that simulates a quantum state.

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 wavefunction 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 = [, , , ]`
`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]]>`
```

`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`.

`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.

`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

### `build`

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`

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`

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 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`

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`

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`

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`

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`

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`

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`

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`

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`

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`

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`

Returns the current weights of the layer.

Returns
Weights values as a list of numpy arrays.

### `set_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`

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, 64]))
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([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.

### `__call__`

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).