qml.devices.default_gaussian.DefaultGaussian

class DefaultGaussian(wires, *, shots=1000, hbar=2, analytic=True)[source]

Bases: pennylane._device.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 (int) – How many times the circuit should be evaluated (or sampled) to estimate the expectation values. If analytic == True, then the number of shots is ignored in the calculation of expectation values and variances, and only controls the number of samples returned by sample.

  • hbar (float) – (default 2) the value of \(\hbar\) in the commutation relation \([\x,\p]=i\hbar\)

  • analytic (bool) – indicates if the device should calculate expectations and variances analytically

author

name

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

shots

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

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

author = 'Xanadu Inc.'
name = 'Default Gaussian PennyLane plugin'
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.Observable]

observables

Get the supported set of observables.

Returns

the set of PennyLane observable names the device supports

Return type

set[str]

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

Get the supported set of operations.

Returns

the set of PennyLane operation names the device supports

Return type

set[str]

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 = '0.11'
short_name = 'default.gaussian'
shots

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

version = '0.11.0'
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

apply(operation, wires, par)

Apply a quantum operation.

capabilities()

Get the other capabilities of the plugin.

check_validity(queue, observables)

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

define_wire_map(wires)

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

execute(queue, observables[, parameters])

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

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.

expval(observable, wires, par)

Returns the expectation value of observable on specified wires.

map_wires(wires)

Map the wire labels of wires using this device’s 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 vector of means and the covariance matrix 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

classmethod capabilities()

Get the other capabilities of the plugin.

Measurements, batching etc.

Returns

results

Return type

dict[str->*]

check_validity(queue, observables)

Checks whether the operations and observables in queue are all supported by the device. Includes checks for inverse operations.

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

  • observables (Iterable[Observable]) – 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

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(queue, observables, parameters={}, **kwargs)

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[Observable]) – 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 value of return_type is not supported

Returns

measured value(s)

Return type

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 Device.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

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

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

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

Parameters

wires (Wires) – requested wires

Returns

means is an array containing the vector of means, and cov is a square array containing the covariance matrix

Return type

tuple (means, cov)

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 Observable 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 iff 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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