qml.devices.default_gaussian.DefaultGaussian¶

class DefaultGaussian(wires, *, shots=None, hbar=2, analytic=None)[source]¶

Bases: Device

Default Gaussian device for PennyLane.

Parameters:

wires (int, Iterable[Number, str]) – Number of subsystems represented by the device, or iterable that contains unique labels for the subsystems as numbers (i.e., [-1, 0, 2]) or strings (['ancilla', 'q1', 'q2']). Default 1 if not specified.
shots (None, int) – How many times the circuit should be evaluated (or sampled) to estimate the expectation values. If None, the results are analytically computed and hence deterministic.
hbar (float) – (default 2) the value of \(\hbar\) in the commutation relation \([\x,\p]=i\hbar\)

Attributes

`analytic`	Whether shots is None or not.
`author`
`name`
`num_executions`	Number of times this device is executed by the evaluation of QNodes running on this device
`obs_queue`	The observables to be measured and returned.
`observables`	Get the supported set of observables.
`op_queue`	The operation queue to be applied.
`operations`	Get the supported set of operations.
`parameters`	Mapping from free parameter index to the list of `Operations` in the device queue that depend on it.
`pennylane_requires`
`short_name`
`shot_vector`	Returns the shot vector, a sparse representation of the shot sequence used by the device when evaluating QNodes.
`shots`	Number of circuit evaluations/random samples used to estimate expectation values of observables
`stopping_condition`	Returns the stopping condition for the device.
`version`
`wire_map`	Ordered dictionary that defines the map from user-provided wire labels to the wire labels used on this device
`wires`	All wires that can be addressed on this device

analytic¶: Whether shots is None or not. Kept for backwards compatability.

author = 'Xanadu Inc.'¶

name = 'Default Gaussian PennyLane plugin'¶

num_executions¶

Number of times this device is executed by the evaluation of QNodes running on this device

Returns:: number of executions
Return type:: int

obs_queue¶

The observables to be measured and returned.

Note that this property can only be accessed within the execution context of execute().

Raises:: ValueError – if outside of the execution context
Returns:: list[~.operation.Operator]

observables¶

op_queue¶

The operation queue to be applied.

Note that this property can only be accessed within the execution context of execute().

Raises:: ValueError – if outside of the execution context
Returns:: list[~.operation.Operation]

operations¶

parameters¶

Mapping from free parameter index to the list of Operations in the device queue that depend on it.

Note that this property can only be accessed within the execution context of execute().

Raises:: ValueError – if outside of the execution context
Returns:: the mapping
Return type:: dict[int->list[ParameterDependency]]

pennylane_requires = '0.42.2'¶

short_name = 'default.gaussian'¶

shot_vector¶

Returns the shot vector, a sparse representation of the shot sequence used by the device when evaluating QNodes.

Example

>>> dev = qml.device("default.mixed", wires=2, shots=[3, 1, 2, 2, 2, 2, 6, 1, 1, 5, 12, 10, 10])
>>> dev.shots
57
>>> dev.shot_vector
[ShotCopies(3 shots x 1),
 ShotCopies(1 shots x 1),
 ShotCopies(2 shots x 4),
 ShotCopies(6 shots x 1),
 ShotCopies(1 shots x 2),
 ShotCopies(5 shots x 1),
 ShotCopies(12 shots x 1),
 ShotCopies(10 shots x 2)]

The sparse representation of the shot sequence is returned, where tuples indicate the number of times a shot integer is repeated.

Type:: list[ShotCopies]

shots¶: Number of circuit evaluations/random samples used to estimate expectation values of observables

stopping_condition¶

Returns the stopping condition for the device. The returned function accepts a queuable object (including a PennyLane operation and observable) and returns True if supported by the device.

Type:: .BooleanFn

version = '0.42.2'¶

wire_map¶: Ordered dictionary that defines the map from user-provided wire labels to the wire labels used on this device

wires¶: All wires that can be addressed on this device

Methods

`apply`(operation, wires, par)	Apply a quantum operation.
`batch_execute`(circuits)	Execute a batch of quantum circuits on the device.
`batch_transform`(circuit)	Apply a differentiable batch transform for preprocessing a circuit prior to execution.
`capabilities`()	Get the capabilities of this device class.
`check_validity`(queue, observables)	Checks whether the operations and observables in queue are all supported by the device.
`custom_expand`(fn)	Register a custom expansion function for the device.
`default_expand_fn`(circuit[, max_expansion])	Method for expanding or decomposing an input circuit.
`define_wire_map`(wires)	Create the map from user-provided wire labels to the wire labels used by the device.
`execute`(operations, observables)	Execute a queue of quantum operations on the device and then measure the given observables.
`execute_and_gradients`(circuits[, method])	Execute a batch of quantum circuits on the device, and return both the results and the gradients.
`execution_context`()	The device execution context used during calls to `execute()`.
`expand`(S, wires)	Expands a Symplectic matrix S to act on the entire subsystem.
`expand_fn`(circuit[, max_expansion])	Method for expanding or decomposing an input circuit.
`expval`(observable, wires, par)	Returns the expectation value of observable on specified wires.
`gradients`(circuits[, method])	Return the gradients of a batch of quantum circuits on the device.
`map_wires`(wires)	Map the wire labels of wires using this device's wire map.
`order_wires`(subset_wires)	Given some subset of device wires return a Wires object with the same wires; sorted according to the device wire map.
`post_apply`()	Called during `execute()` after the individual operations have been executed.
`post_measure`()	Called during `execute()` after the individual observables have been measured.
`pre_apply`()	Called during `execute()` before the individual operations are executed.
`pre_measure`()	Called during `execute()` before the individual observables are measured.
`probability`([wires])	Return the (marginal) probability of each computational basis state from the last run of the device.
`reduced_state`(wires)	Returns the covariance matrix and the vector of means of the specified wires.
`reset`()	Reset the device
`sample`(observable, wires, par)	Return a sample of an observable.
`supports_observable`(observable)	Checks if an observable is supported by this device. Raises a ValueError,
`supports_operation`(operation)	Checks if an operation is supported by this device.
`var`(observable, wires, par)	Returns the variance of observable on specified wires.

apply(operation, wires, par)[source]¶

Apply a quantum operation.

For plugin developers: this function should apply the operation on the device.

Parameters:

operation (str) – name of the operation
wires (Wires) – wires that the operation is applied to
par (tuple) – parameters for the operation

batch_execute(circuits)[source]¶

Execute a batch of quantum circuits on the device.

The circuits are represented by tapes, and they are executed one-by-one using the device’s execute method. The results are collected in a list.

For plugin developers: This function should be overwritten if the device can efficiently run multiple circuits on a backend, for example using parallel and/or asynchronous executions.

Parameters:: circuits (list[.tape.QuantumTape]) – circuits to execute on the device
Returns:: list of measured value(s)
Return type:: list[array[float]]

batch_transform(circuit)¶

Apply a differentiable batch transform for preprocessing a circuit prior to execution. This method is called directly by the QNode, and should be overwritten if the device requires a transform that generates multiple circuits prior to execution.

By default, this method contains logic for generating multiple circuits, one per term, of a circuit that terminates in expval(H), if the underlying device does not support Hamiltonian expectation values, or if the device requires finite shots.

Warning

This method will be tracked by autodifferentiation libraries, such as Autograd, JAX, TensorFlow, and Torch. Please make sure to use qml.math for autodiff-agnostic tensor processing if required.

Parameters:: circuit (.QuantumTape) – the circuit to preprocess
Returns:: Returns a tuple containing the sequence of circuits to be executed, and a post-processing function to be applied to the list of evaluated circuit results.
Return type:: tuple[Sequence[.QuantumTape], callable]

classmethod capabilities()[source]¶

Get the capabilities of this device class.

Inheriting classes that change or add capabilities must override this method, for example via

@classmethod
def capabilities(cls):
    capabilities = super().capabilities().copy()
    capabilities.update(
        supports_a_new_capability=True,
    )
    return capabilities

Returns:: results
Return type:: dict[str->*]

check_validity(queue, observables)¶

Checks whether the operations and observables in queue are all supported by the device.

Parameters:

queue (Iterable[Operation]) – quantum operation objects which are intended to be applied on the device
observables (Iterable[Operator]) – observables which are intended to be evaluated on the device

Raises:

DeviceError – if there are operations in the queue or observables that the device does not support

custom_expand(fn)¶

Register a custom expansion function for the device.

Example

@dev.custom_expand
def my_expansion_function(self, tape, max_expansion=10):
    ...
    # can optionally call the default device expansion
    tape = self.default_expand_fn(tape, max_expansion=max_expansion)
    return tape

The custom device expansion function must have arguments self (the device object), tape (the input circuit to transform and execute), and max_expansion (the number of times the circuit should be expanded).

The default default_expand_fn() method of the original device may be called. It is highly recommended to call this before returning, to ensure that the expanded circuit is supported on the device.

default_expand_fn(circuit, max_expansion=10)¶

Method for expanding or decomposing an input circuit. This method should be overwritten if custom expansion logic is required.

By default, this method expands the tape if:

state preparation operations are called mid-circuit,
nested tapes are present,
any operations are not supported on the device, or
multiple observables are measured on the same wire.

Parameters:

circuit (.QuantumTape) – the circuit to expand.
max_expansion (int) – The number of times the circuit should be expanded. Expansion occurs when an operation or measurement is not supported, and results in a gate decomposition. If any operations in the decomposition remain unsupported by the device, another expansion occurs.

Returns:

The expanded/decomposed circuit, such that the device will natively support all operations.

Return type:

.QuantumTape

define_wire_map(wires)¶

Create the map from user-provided wire labels to the wire labels used by the device.

The default wire map maps the user wire labels to wire labels that are consecutive integers.

However, by overwriting this function, devices can specify their preferred, non-consecutive and/or non-integer wire labels.

Parameters:: wires (Wires) – user-provided wires for this device
Returns:: dictionary specifying the wire map
Return type:: OrderedDict

Example

>>> dev = device('my.device', wires=['b', 'a'])
>>> dev.wire_map()
OrderedDict( [(Wires(['a']), Wires([0])), (Wires(['b']), Wires([1]))])

execute(operations, observables)[source]¶

Execute a queue of quantum operations on the device and then measure the given observables.

For plugin developers: Instead of overwriting this, consider implementing a suitable subset of pre_apply(), apply(), post_apply(), pre_measure(), expval(), var(), sample(), post_measure(), and execution_context().

Parameters:

queue (Iterable[Operation]) – operations to execute on the device
observables (Iterable[Operator]) – observables to measure and return
parameters (dict[int, list[ParameterDependency]]) – Mapping from free parameter index to the list of Operations (in the queue) that depend on it.

Keyword Arguments:

return_native_type (bool) – If True, return the result in whatever type the device uses internally, otherwise convert it into array[float]. Default: False.

Raises:

QuantumFunctionError – if the observable is not supported

Returns:

measured value(s)

Return type:

array[float]

execute_and_gradients(circuits, method='jacobian', **kwargs)¶

Execute a batch of quantum circuits on the device, and return both the results and the gradients.

The circuits are represented by tapes, and they are executed one-by-one using the device’s execute method. The results and the corresponding Jacobians are collected in a list.

For plugin developers: This method should be overwritten if the device can efficiently run multiple circuits on a backend, for example using parallel and/or asynchronous executions, and return both the results and the Jacobians.

Parameters:

circuits (list[.tape.QuantumTape]) – circuits to execute on the device
method (str) – the device method to call to compute the Jacobian of a single circuit
**kwargs – keyword argument to pass when calling method

Returns:

Tuple containing list of measured value(s) and list of Jacobians. Returned Jacobians should be of shape (output_shape, num_params).

Return type:

tuple[list[array[float]], list[array[float]]]

execution_context()¶

The device execution context used during calls to execute().

You can overwrite this function to return a context manager in case your quantum library requires that; all operations and method calls (including apply() and expval()) are then evaluated within the context of this context manager (see the source of execute() for more details).

expand(S, wires)[source]¶

Expands a Symplectic matrix S to act on the entire subsystem.

Parameters:

S (array) – a \(2M\times 2M\) Symplectic matrix
wires (Wires) – wires of the modes that S acts on

Returns:

the resulting \(2N\times 2N\) Symplectic matrix

Return type:

array

expand_fn(circuit, max_expansion=10)¶

Method for expanding or decomposing an input circuit. Can be the default or a custom expansion method, see Device.default_expand_fn() and Device.custom_expand() for more details.

Parameters:

circuit (.QuantumTape) – the circuit to expand.
max_expansion (int) – The number of times the circuit should be expanded. Expansion occurs when an operation or measurement is not supported, and results in a gate decomposition. If any operations in the decomposition remain unsupported by the device, another expansion occurs.

Returns:

The expanded/decomposed circuit, such that the device will natively support all operations.

Return type:

.QuantumTape

expval(observable, wires, par)[source]¶

Returns the expectation value of observable on specified wires.

Note: all arguments accept _lists_, which indicate a tensor product of observables.

Parameters:

observable (str or list[str]) – name of the observable(s)
wires (Wires) – wires the observable(s) are to be measured on
par (tuple or list[tuple]]) – parameters for the observable(s)

Returns:

expectation value \(\expect{A} = \bra{\psi}A\ket{\psi}\)

Return type:

float

gradients(circuits, method='jacobian', **kwargs)¶

Return the gradients of a batch of quantum circuits on the device.

The gradient method method is called sequentially for each circuit, and the corresponding Jacobians are collected in a list.

For plugin developers: This method should be overwritten if the device can efficiently compute the gradient of multiple circuits on a backend, for example using parallel and/or asynchronous executions.

Parameters:

circuits (list[.tape.QuantumTape]) – circuits to execute on the device
method (str) – the device method to call to compute the Jacobian of a single circuit
**kwargs – keyword argument to pass when calling method

Returns:

List of Jacobians. Returned Jacobians should be of shape (output_shape, num_params).

Return type:

list[array[float]]

map_wires(wires)¶

Map the wire labels of wires using this device’s wire map.

Parameters:: wires (Wires) – wires whose labels we want to map to the device’s internal labelling scheme
Returns:: wires with new labels
Return type:: Wires

order_wires(subset_wires)¶

Given some subset of device wires return a Wires object with the same wires; sorted according to the device wire map.

Parameters:: subset_wires (Wires) – The subset of device wires (in any order).
Raises:: ValueError – Could not find some or all subset wires subset_wires in device wires device_wires.
Returns:: a new Wires object containing the re-ordered wires set
Return type:: ordered_wires (Wires)

post_apply()¶: Called during execute() after the individual operations have been executed.

post_measure()¶: Called during execute() after the individual observables have been measured.

pre_apply()[source]¶: Called during execute() before the individual operations are executed.

pre_measure()¶: Called during execute() before the individual observables are measured.

probability(wires=None)¶

Return the (marginal) probability of each computational basis state from the last run of the device.

Parameters:: wires (Sequence[int]) – Sequence of wires to return marginal probabilities for. Wires not provided are traced out of the system.
Returns:: Dictionary mapping a tuple representing the state to the resulting probability. The dictionary should be sorted such that the state tuples are in lexicographical order.
Return type:: OrderedDict[tuple, float]

reduced_state(wires)[source]¶

Returns the covariance matrix and the vector of means of the specified wires.

Parameters:: wires (Wires) – requested wires
Returns:: cov is a square array containing the covariance matrix, and means is an array containing the vector of means
Return type:: tuple (cov, means)

reset()[source]¶: Reset the device

sample(observable, wires, par)[source]¶

Return a sample of an observable.

Note

The default.gaussian plugin only supports sampling from X, P, and QuadOperator observables.

Parameters:

observable (str) – name of the observable
wires (Wires) – wires the observable is to be measured on
par (tuple) – parameters for the observable

Returns:

samples in an array of dimension (n, num_wires)

Return type:

array[float]

supports_observable(observable)¶

Checks if an observable is supported by this device. Raises a ValueError,: if not a subclass or string of an Observable was passed.

Parameters:: observable (type or str) – observable to be checked
Raises:: ValueError – if observable is not a Operator class or string
Returns:: True iff supplied observable is supported
Return type:: bool

supports_operation(operation)¶

Checks if an operation is supported by this device.

Parameters:: operation (type or str) – operation to be checked
Raises:: ValueError – if operation is not a Operation class or string
Returns:: True if supplied operation is supported
Return type:: bool

var(observable, wires, par)[source]¶

Returns the variance of observable on specified wires.

Note: all arguments support _lists_, which indicate a tensor product of observables.

Parameters:

observable (str or list[str]) – name of the observable(s)
wires (Wires) – wires the observable(s) is to be measured on
par (tuple or list[tuple]]) – parameters for the observable(s)

Raises:

NotImplementedError – if the device does not support variance computation

Returns:

variance \(\mathrm{var}(A) = \bra{\psi}A^2\ket{\psi} - \bra{\psi}A\ket{\psi}^2\)

Return type:

float

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