qml.devices.default_qubit.DefaultQubit¶

class DefaultQubit(wires, *, shots=1000, analytic=True, cache=0)[source]¶

Bases: pennylane._qubit_device.QubitDevice

Default qubit 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. Defaults to 1000 if not specified. 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.
analytic (bool) – indicates if the device should calculate expectations and variances analytically
cache (int) – Number of device executions to store in a cache to speed up subsequent executions. A value of 0 indicates that no caching will take place. Once filled, older elements of the cache are removed and replaced with the most recent device executions to keep the cache up to date.

Attributes

`author`
`cache`	Number of device executions to store in a cache to speed up subsequent executions.
`circuit_hash`	The hash of the circuit upon the last execution.
`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`
`op_queue`	The operation queue to be applied.
`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
`state`	Returns the state vector of the circuit prior to measurement.
`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.'¶

cache¶

Number of device executions to store in a cache to speed up subsequent executions. If set to zero, no caching occurs.

Type: int

circuit_hash¶

The hash of the circuit upon the last execution.

This can be used by devices in apply() for parametric compilation.

name = 'Default qubit 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.Observable]

observables = {'Hadamard', 'Hermitian', 'Identity', 'PauliX', 'PauliY', 'PauliZ'}¶

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 = {'BasisState', 'CNOT', 'CRX', 'CRY', 'CRZ', 'CRot', 'CSWAP', 'CY', 'CZ', 'DiagonalQubitUnitary', 'Hadamard', 'MultiRZ', 'PauliX', 'PauliY', 'PauliZ', 'PhaseShift', 'QubitStateVector', 'QubitUnitary', 'RX', 'RY', 'RZ', 'Rot', 'S', 'SWAP', 'SX', 'T', 'Toffoli'}¶

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 = '0.13'¶

short_name = 'default.qubit'¶

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

state¶: Returns the state vector of the circuit prior to measurement.

Note

Only state vector simulators support this property. Please see the plugin documentation for more details.

version = '0.13.0'¶

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

Methods

`access_state`([wires])	Check that the device has access to an internal state and return it if available.
`active_wires`(operators)	Returns the wires acted on by a set of operators.
`analytic_probability`([wires])	Return the (marginal) probability of each computational basis state from the last run of the device.
`apply`(operations[, rotations])	Apply quantum operations, rotate the circuit into the measurement basis, and compile and execute the quantum circuit.
`batch_execute`(circuits)	Execute a batch of quantum circuits on the device.
`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.
`define_wire_map`(wires)	Create the map from user-provided wire labels to the wire labels used by the device.
`density_matrix`(wires)	Returns the reduced density matrix of a given set of wires.
`estimate_probability`([wires])	Return the estimated probability of each computational basis state using the generated samples.
`execute`(circuit, **kwargs)	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()`.
`expval`(observable)	Returns the expectation value of observable on specified wires.
`generate_basis_states`(num_wires[, dtype])	Generates basis states in binary representation according to the number of wires specified.
`generate_samples`()	Returns the computational basis samples generated for all wires.
`map_wires`(wires)	Map the wire labels of wires using this device’s wire map.
`marginal_prob`(prob[, wires])	Return the marginal probability of the computational basis states by summing the probabiliites on the non-specified 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`()	Called during `execute()` before the individual operations are executed.
`pre_measure`()	Called during `execute()` before the individual observables are measured.
`probability`([wires])	Return either the analytic probability or estimated probability of each computational basis state.
`reset`()	Reset the device
`sample`(observable)	Return a sample of an observable.
`sample_basis_states`(number_of_states, …)	Sample from the computational basis states based on the state probability.
`states_to_binary`(samples, num_wires[, dtype])	Convert basis states from base 10 to binary representation.
`statistics`(observables)	Process measurement results from circuit execution and return statistics.
`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)	Returns the variance of observable on specified wires.

access_state(wires=None)¶

Check that the device has access to an internal state and return it if available.

Parameters: wires (Wires) – wires of the reduced system
Raises: QuantumFunctionError – if the device is not capable of returning the state
Returns: the state or the density matrix of the device
Return type: array or tensor

static active_wires(operators)¶

Returns the wires acted on by a set of operators.

Parameters: operators (list[Operation]) – operators for which we are gathering the active wires
Returns: wires activated by the specified operators
Return type: Wires

analytic_probability(wires=None)[source]¶

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

PennyLane uses the convention \(|q_0,q_1,\dots,q_{N-1}\rangle\) where \(q_0\) is the most significant bit.

If no wires are specified, then all the basis states representable by the device are considered and no marginalization takes place.

Note

marginal_prob() may be used as a utility method to calculate the marginal probability distribution.

Parameters: wires (Iterable[Number, str], Number, str, Wires) – wires to return marginal probabilities for. Wires not provided are traced out of the system.
Returns: list of the probabilities
Return type: List[float]

apply(operations, rotations=None, **kwargs)[source]¶

Apply quantum operations, rotate the circuit into the measurement basis, and compile and execute the quantum circuit.

This method receives a list of quantum operations queued by the QNode, and should be responsible for:

Constructing the quantum program
(Optional) Rotating the quantum circuit using the rotation operations provided. This diagonalizes the circuit so that arbitrary observables can be measured in the computational basis.
Compile the circuit
Execute the quantum circuit

Both arguments are provided as lists of PennyLane Operation instances. Useful properties include name, wires, and parameters, and inverse:

>>> op = qml.RX(0.2, wires=[0])
>>> op.name # returns the operation name
"RX"
>>> op.wires # returns a Wires object representing the wires that the operation acts on
<Wires = [0]>
>>> op.parameters # returns a list of parameters
[0.2]
>>> op.inverse # check if the operation should be inverted
False
>>> op = qml.RX(0.2, wires=[0]).inv
>>> op.inverse
True

Parameters

operations (list[Operation]) – operations to apply to the device

Keyword Arguments

rotations (list[Operation]) – operations that rotate the circuit pre-measurement into the eigenbasis of the observables.
hash (int) – the hash value of the circuit constructed by CircuitGraph.hash

batch_execute(circuits)¶

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

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_inverse_operations=False,
        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. 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]>)])

density_matrix(wires)[source]¶

Returns the reduced density matrix of a given set of wires.

Parameters: wires (Wires) – wires of the reduced system.
Returns: complex tensor of shape (2 ** len(wires), 2 ** len(wires)) representing the reduced density matrix.
Return type: array[complex]

estimate_probability(wires=None)¶

Return the estimated probability of each computational basis state using the generated samples.

Parameters: wires (Iterable[Number, str], Number, str, Wires) – wires to calculate marginal probabilities for. Wires not provided are traced out of the system.
Returns: list of the probabilities
Return type: List[float]

execute(circuit, **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

apply()
generate_samples()
probability()

Additional keyword arguments may be passed to the this method that can be utilised by apply(). An example would be passing the QNode hash that can be used later for parametric compilation.

Parameters: circuit (CircuitGraph) – circuit to execute on the device
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).

expval(observable)¶

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

static generate_basis_states(num_wires, dtype=<class 'numpy.uint32'>)¶

Generates basis states in binary representation according to the number of wires specified.

The states_to_binary method creates basis states faster (for larger systems at times over x25 times faster) than the approach using itertools.product, at the expense of using slightly more memory.

Due to the large size of the integer arrays for more than 32 bits, memory allocation errors may arise in the states_to_binary method. Hence we constraint the dtype of the array to represent unsigned integers on 32 bits. Due to this constraint, an overflow occurs for 32 or more wires, therefore this approach is used only for fewer wires.

For smaller number of wires speed is comparable to the next approach (using itertools.product), hence we resort to that one for testing purposes.

Parameters

num_wires (int) – the number wires
dtype=np.uint32 (type) – the data type of the arrays to use

Returns

the sampled basis states

Return type

np.ndarray

generate_samples()¶

Returns the computational basis samples generated for all wires.

Note that PennyLane uses the convention \(|q_0,q_1,\dots,q_{N-1}\rangle\) where \(q_0\) is the most significant bit.

Warning

This method should be overwritten on devices that generate their own computational basis samples, with the resulting computational basis samples stored as self._samples.

Returns: array of samples in the shape (dev.shots, dev.num_wires)
Return type: array[complex]

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

marginal_prob(prob, wires=None)¶

Return the marginal probability of the computational basis states by summing the probabiliites on the non-specified wires.

If no wires are specified, then all the basis states representable by the device are considered and no marginalization takes place.

Note

If the provided wires are not in the order as they appear on the device, the returned marginal probabilities take this permutation into account.

For example, if the addressable wires on this device are Wires([0, 1, 2]) and this function gets passed wires=[2, 0], then the returned marginal probability vector will take this ‘reversal’ of the two wires into account:

\[\mathbb{P}^{(2, 0)} = \left[ |00\rangle, |10\rangle, |01\rangle, |11\rangle \right]\]

Parameters

prob – The probabilities to return the marginal probabilities for
wires (Iterable[Number, str], Number, str, Wires) – wires to return marginal probabilities for. Wires not provided are traced out of the system.

Returns

array of the resulting marginal probabilities.

Return type

array[float]

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=None)¶

Return either the analytic probability or estimated probability of each computational basis state.

If no analytic attributes exists for the device, then return the estimated probability.

Parameters: wires (Iterable[Number, str], Number, str, Wires) – wires to return marginal probabilities for. Wires not provided are traced out of the system.
Returns: list of the probabilities
Return type: List[float]

reset()[source]¶: Reset the device

sample(observable)¶

Return a sample of an observable.

The number of samples is determined by the value of Device.shots, which can be directly modified.

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 sampling

Returns

samples in an array of dimension (shots,)

Return type

array[float]

sample_basis_states(number_of_states, state_probability)¶

Sample from the computational basis states based on the state probability.

This is an auxiliary method to the generate_samples method.

Parameters: number_of_states (int) – the number of basis states to sample from
Returns: the sampled basis states
Return type: List[int]

static states_to_binary(samples, num_wires, dtype=<class 'numpy.int64'>)¶

Convert basis states from base 10 to binary representation.

This is an auxiliary method to the generate_samples method.

Parameters

samples (List[int]) – samples of basis states in base 10 representation
num_wires (int) – the number of qubits
dtype (type) – Type of the internal integer array to be used. Can be important to specify for large systems for memory allocation purposes.

Returns

basis states in binary representation

Return type

List[int]

statistics(observables)¶

Process measurement results from circuit execution and return statistics.

This includes returning expectation values, variance, samples, probabilities, states, and density matrices.

Parameters: observables (List[Observable]) – the observables to be measured
Raises: QuantumFunctionError – if the value of return_type is not supported
Returns: the corresponding statistics
Return type: Union[float, List[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)¶

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

qml.devices.default_qubit.DefaultQubit¶

Attributes

Methods

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