qml.devices.default_qubit_torch.DefaultQubitTorch¶

class
DefaultQubitTorch
(wires, *, shots=None, analytic=None, torch_device=None)[source]¶ Bases:
pennylane.devices.default_qubit.DefaultQubit
Simulator plugin based on
"default.qubit"
, written using PyTorch.Short name:
default.qubit.torch
This device provides a purestate qubit simulator written using PyTorch. As a result, it supports classical backpropagation as a means to compute the Jacobian. This can be faster than the parametershift rule for analytic quantum gradients when the number of parameters to be optimized is large.
To use this device, you will need to install PyTorch:
pip install torch>=1.8.0
Example
The
default.qubit.torch
is designed to be used with endtoend classical backpropagation (diff_method="backprop"
) and the PyTorch interface. This is the default method of differentiation when creating a QNode with this device.Using this method, the created QNode is a ‘whitebox’, and is tightly integrated with your PyTorch computation:
dev = qml.device("default.qubit.torch", wires=1) @qml.qnode(dev, interface="torch", diff_method="backprop") def circuit(x): qml.RX(x[1], wires=0) qml.Rot(x[0], x[1], x[2], wires=0) return qml.expval(qml.PauliZ(0))
>>> weights = torch.tensor([0.2, 0.5, 0.1], requires_grad=True) >>> res = circuit(weights) >>> res.backward() >>> print(weights.grad) tensor([2.2527e01, 1.0086e+00, 1.3878e17])
Autograd mode will also work when using classical backpropagation:
>>> def cost(weights): ... return torch.sum(circuit(weights)**3)  1 >>> res = circuit(weights) >>> res.backward() >>> print(weights.grad) tensor([4.5053e01, 2.0173e+00, 5.9837e17])
Executing the pipeline in PyTorch will allow the whole computation to be run on the GPU, and therefore providing an acceleration. Your parameters need to be instantiated on the same device as the backend device.
dev = qml.device("default.qubit.torch", wires=1, torch_device='cuda') @qml.qnode(dev, interface="torch", diff_method="backprop") def circuit(x): qml.RX(x[1], wires=0) qml.Rot(x[0], x[1], x[2], wires=0) return qml.expval(qml.PauliZ(0))
>>> weights = torch.tensor([0.2, 0.5, 0.1], requires_grad=True, device='cuda') >>> res = circuit(weights) >>> res.backward() >>> print(weights.grad) tensor([2.2527e01, 1.0086e+00, 2.9919e17], device='cuda:0')
There are a couple of things to keep in mind when using the
"backprop"
differentiation method for QNodes:You must use the
"torch"
interface for classical backpropagation, as PyTorch is used as the device backend.Only exact expectation values, variances, and probabilities are differentiable. When instantiating the device with
shots!=None
, differentiating QNode outputs will result in an error.
If you wish to use a different machinelearning interface, or prefer to calculate quantum gradients using the
parametershift
orfinitediff
differentiation methods, consider using thedefault.qubit
device instead. Parameters
wires (int, Iterable) – Number of subsystems represented by the device, or iterable that contains unique labels for the subsystems. Default 1 if not specified.
shots (None, int) – How many times the circuit should be evaluated (or sampled) to estimate the expectation values. Defaults to
None
if not specified, which means that the device returns analytical results. Ifshots > 0
is used, thediff_method="backprop"
QNode differentiation method is not supported and it is recommended to consider switching device todefault.qubit
and usingdiff_method="parametershift"
.torch_device='cpu' (str) – the device on which the computation will be
run –
e.g. –
or 'cuda' ('cpu') –
Attributes
Whether shots is None or not.
The hash of the circuit upon the last execution.
Mapping used to override the logic of measurement processes.
Number of times this device is executed by the evaluation of QNodes running on this device
The observables to be measured and returned.
The operation queue to be applied.
Mapping from free parameter index to the list of
Operations
in the device queue that depend on it.Returns the shot vector, a sparse representation of the shot sequence used by the device when evaluating QNodes.
Number of circuit evaluations/random samples used to estimate expectation values of observables
Returns the state vector of the circuit prior to measurement.
Returns the stopping condition for the device.
Ordered dictionary that defines the map from userprovided wire labels to the wire labels used on this device
All wires that can be addressed on this device

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

circuit_hash
¶ The hash of the circuit upon the last execution.
This can be used by devices in
apply()
for parametric compilation.

measurement_map
= {}¶ Mapping used to override the logic of measurement processes. The dictionary maps a measurement class to a string containing the name of a device’s method that overrides the measurement process. The method defined by the device should have the following arguments:
measurement (MeasurementProcess): measurement to override
 shot_range (tuple[int]): 2tuple of integers specifying the range of samples
to use. If not specified, all samples are used.
 bin_size (int): Divides the shot range into bins of size
bin_size
, and returns the measurement statistic separately over each bin. If not provided, the entire shot range is treated as a single bin.
 bin_size (int): Divides the shot range into bins of size
Note
When overriding the logic of a
MeasurementTransform
, the method defined by the device should only have a single argument:tape: quantum tape to transform
Example:
Let’s create device that inherits from
DefaultQubit
and overrides the logic of the qml.sample measurement. To do so we will need to update themeasurement_map
dictionary:class NewDevice(DefaultQubit): def __init__(self, wires, shots): super().__init__(wires=wires, shots=shots) self.measurement_map[SampleMP] = "sample_measurement" def sample_measurement(self, measurement, shot_range=None, bin_size=None): return 2
>>> dev = NewDevice(wires=2, shots=1000) >>> @qml.qnode(dev) ... def circuit(): ... return qml.sample() >>> circuit() tensor(2, requires_grad=True)

name
= 'Default qubit (Torch) 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
= {'Evolution', 'Exp', 'Hadamard', 'Hamiltonian', 'Hermitian', 'Identity', 'PauliX', 'PauliY', 'PauliZ', 'Prod', 'Projector', 'SProd', 'SparseHamiltonian', 'Sum'}¶

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
= {'Adjoint(ISWAP)', 'Adjoint(S)', 'Adjoint(SISWAP)', 'Adjoint(SX)', 'Adjoint(T)', 'BasisState', 'BlockEncode', 'CCZ', 'CH', 'CNOT', 'CPhase', 'CPhaseShift00', 'CPhaseShift01', 'CPhaseShift10', 'CRX', 'CRY', 'CRZ', 'CRot', 'CSWAP', 'CY', 'CZ', 'ControlledPhaseShift', 'ControlledQubitUnitary', 'DiagonalQubitUnitary', 'DoubleExcitation', 'DoubleExcitationMinus', 'DoubleExcitationPlus', 'ECR', 'Hadamard', 'ISWAP', 'Identity', 'IntegerComparator', 'IsingXX', 'IsingXY', 'IsingYY', 'IsingZZ', 'MultiControlledX', 'MultiRZ', 'OrbitalRotation', 'PCPhase', 'PSWAP', 'PauliX', 'PauliY', 'PauliZ', 'PhaseShift', 'QFT', 'QubitCarry', 'QubitStateVector', 'QubitSum', 'QubitUnitary', 'RX', 'RY', 'RZ', 'Rot', 'S', 'SISWAP', 'SQISW', 'SWAP', 'SX', 'SingleExcitation', 'SingleExcitationMinus', 'SingleExcitationPlus', 'Snapshot', '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
= '0.31.0dev'¶

short_name
= 'default.qubit.torch'¶

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.qubit", wires=2, shots=[3, 1, 2, 2, 2, 2, 6, 1, 1, 5, 12, 10, 10]) >>> dev.shots 57 >>> dev.shot_vector [ShotTuple(shots=3, copies=1), ShotTuple(shots=1, copies=1), ShotTuple(shots=2, copies=4), ShotTuple(shots=6, copies=1), ShotTuple(shots=1, copies=2), ShotTuple(shots=5, copies=1), ShotTuple(shots=12, copies=1), ShotTuple(shots=10, copies=2)]
The sparse representation of the shot sequence is returned, where tuples indicate the number of times a shot integer is repeated.
 Type
list[ShotTuple[int, int]]

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

state
¶

stopping_condition
¶

version
= '0.31.0dev'¶

wire_map
¶ Ordered dictionary that defines the map from userprovided 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.
adjoint_jacobian
(tape[, starting_state, …])Implements the adjoint method outlined in Jones and Gacon to differentiate an input tape.
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.
batch_transform
(circuit)Apply a differentiable batch transform for preprocessing a circuit prior to execution.
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.
classical_shadow
(obs, circuit)Returns the measured bits and recipes in the classical shadow protocol.
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 userprovided wire labels to the wire labels used by the device.
density_matrix
(wires)Returns the reduced density matrix over the given wires.
estimate_probability
([wires, shot_range, …])Return the estimated probability of each computational basis state using the generated samples.
execute
(circuit, **kwargs)It executes 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.
The device execution context used during calls to
execute()
.expand_fn
(circuit[, max_expansion])Method for expanding or decomposing an input circuit.
expval
(observable[, shot_range, bin_size])Returns the expectation value of a Hamiltonian observable.
generate_basis_states
(num_wires[, dtype])Generates basis states in binary representation according to the number of wires specified.
Returns the computational basis samples generated for all 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.
marginal_prob
(prob[, wires])Return the marginal probability of the computational basis states by summing the probabiliites on the nonspecified wires.
mutual_info
(wires0, wires1, log_base)Returns the mutual information prior to measurement:
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.
Called during
execute()
after the individual operations have been executed.Called during
execute()
after the individual observables have been measured.Called during
execute()
before the individual operations are executed.Called during
execute()
before the individual observables are measured.probability
([wires, shot_range, bin_size])Return either the analytic probability or estimated probability of each computational basis state.
reset
()Reset the device
sample
(observable[, shot_range, bin_size, …])Return samples of an observable.
sample_basis_states
(number_of_states, …)Sample from the computational basis states based on the state probability.
shadow_expval
(obs, circuit)Compute expectation values using classical shadows in a differentiable manner.
shot_vec_statistics
(circuit)Process measurement results from circuit execution using a device with a shot vector and return statistics.
states_to_binary
(samples, num_wires[, dtype])Convert basis states from base 10 to binary representation.
statistics
(circuit[, shot_range, bin_size])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[, shot_range, bin_size])Returns the variance of observable on specified wires.
vn_entropy
(wires, log_base)Returns the Von Neumann entropy prior to measurement.

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

adjoint_jacobian
(tape, starting_state=None, use_device_state=False)¶ Implements the adjoint method outlined in Jones and Gacon to differentiate an input tape.
After a forward pass, the circuit is reversed by iteratively applying adjoint gates to scan backwards through the circuit.
Note
The adjoint differentiation method has the following restrictions:
As it requires knowledge of the statevector, only statevector simulator devices can be used.
Only expectation values are supported as measurements.
Does not work for parametrized observables like
Hamiltonian
orHermitian
.
 Parameters
tape (QuantumTape) – circuit that the function takes the gradient of
 Keyword Arguments
starting_state (tensor_like) – postforward pass state to start execution with. It should be complexvalued. Takes precedence over
use_device_state
.use_device_state (bool) – use current device state to initialize. A forward pass of the same circuit should be the last thing the device has executed. If a
starting_state
is provided, that takes precedence.
 Returns
the derivative of the tape with respect to trainable parameters. Dimensions are
(len(observables), len(trainable_params))
. Return type
array or tuple[array]
 Raises
QuantumFunctionError – if the input tape has measurements that are not expectation values or contains a multiparameter operation aside from
Rot

analytic_probability
(wires=None)¶ Return the (marginal) probability of each computational basis state from the last run of the device.
PennyLane uses the convention \(q_0,q_1,\dots,q_{N1}\rangle\) 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
array[float]

apply
(operations, rotations=None, **kwargs)¶ 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 includename
,wires
, andparameters
:>>> 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]
 Parameters
operations (list[Operation]) – operations to apply to the device
 Keyword Arguments
rotations (list[Operation]) – operations that rotate the circuit premeasurement 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 onebyone 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[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 autodiffagnostic 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 postprocessing 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[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

classical_shadow
(obs, circuit)¶ Returns the measured bits and recipes in the classical shadow protocol.
The protocol is described in detail in the classical shadows paper. This measurement process returns the randomized Pauli measurements (the
recipes
) that are performed for each qubit and snapshot as an integer:0 for Pauli X,
1 for Pauli Y, and
2 for Pauli Z.
It also returns the measurement results (the
bits
); 0 if the 1 eigenvalue is sampled, and 1 if the 1 eigenvalue is sampled.The device shots are used to specify the number of snapshots. If
T
is the number of shots andn
is the number of qubits, then both the measured bits and the Pauli measurements have shape(T, n)
.This implementation leverages vectorization and offers a significant speedup over the generic implementation.
Note
This method internally calls
np.einsum
which supports at most 52 indices, thus the classical shadow measurement for this device supports at most 52 qubits.See also
 Parameters
obs (ClassicalShadowMP) – The classical shadow measurement process
circuit (QuantumTape) – The quantum tape that is being executed
 Returns
A tensor with shape
(2, T, n)
, where the first row represents the measured bits and the second represents the recipes used. Return type
tensor_like[int]

custom_expand
(fn)¶ Register a custom expansion function for the device.
Example
dev = qml.device("default.qubit", wires=2) @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), andmax_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:
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

define_wire_map
(wires)¶ Create the map from userprovided 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, nonconsecutive and/or noninteger wire labels.
 Parameters
wires (Wires) – userprovided 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)¶ Returns the reduced density matrix over the given wires.
 Parameters
wires (Wires) – wires of the reduced system
 Returns
complex array of shape
(2 ** len(wires), 2 ** len(wires))
representing the reduced density matrix of the state prior to measurement. Return type
array[complex]

estimate_probability
(wires=None, shot_range=None, bin_size=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.
shot_range (tuple[int]) – 2tuple of integers specifying the range of samples to use. If not specified, all samples are used.
bin_size (int) – Divides the shot range into bins of size
bin_size
, and returns the measurement statistic separately over each bin. If not provided, the entire shot range is treated as a single bin.
 Returns
list of the probabilities
 Return type
array[float]

execute
(circuit, **kwargs)[source]¶ It executes 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
Additional keyword arguments may be passed to the this method that can be utilised by
apply()
. An example would be passing theQNode
hash that can be used later for parametric compilation. Parameters
circuit (QuantumTape) – 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]

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 onebyone 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()
andexpval()
) are then evaluated within the context of this context manager (see the source ofexecute()
for more details).

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()
andDevice.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

expval
(observable, shot_range=None, bin_size=None)¶ Returns the expectation value of a Hamiltonian observable. When the observable is a
Hamiltonian
orSparseHamiltonian
object, the expectation value is computed directly from the sparse matrix representation, which leads to faster execution. Parameters
observable (Observable) – a PennyLane observable
shot_range (tuple[int]) – 2tuple of integers specifying the range of samples to use. If not specified, all samples are used.
bin_size (int) – Divides the shot range into bins of size
bin_size
, and returns the measurement statistic separately over each bin. If not provided, the entire shot range is treated as a single bin.
 Returns
returns the expectation value of the observable
 Return type
float
Warning
This function does not support broadcasting if
observable
is a :class:~.Hamiltonian
and the device interface or the interface of the Hamiltonian is not NumPy or Autograd

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
array[int]

generate_samples
()¶ Returns the computational basis samples generated for all wires.
Note that PennyLane uses the convention \(q_0,q_1,\dots,q_{N1}\rangle\) 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]

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.

marginal_prob
(prob, wires=None)¶ Return the marginal probability of the computational basis states by summing the probabiliites on the nonspecified 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 passedwires=[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]

mutual_info
(wires0, wires1, log_base)¶ Returns the mutual information prior to measurement:
\[I(A, B) = S(\rho^A) + S(\rho^B)  S(\rho^{AB})\]where \(S\) is the von Neumann entropy.

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.

probability
(wires=None, shot_range=None, bin_size=None)¶ Return either the analytic probability or estimated probability of each computational basis state.
Devices that require a finite number of shots always 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
array[float]

reset
()¶ Reset the device

sample
(observable, shot_range=None, bin_size=None, counts=False)¶ Return samples of an observable.
 Parameters
observable (Observable) – the observable to sample
shot_range (tuple[int]) – 2tuple of integers specifying the range of samples to use. If not specified, all samples are used.
bin_size (int) – Divides the shot range into bins of size
bin_size
, and returns the measurement statistic separately over each bin. If not provided, the entire shot range is treated as a single bin.counts (bool) – whether counts (
True
) or raw samples (False
) should be returned
 Raises
EigvalsUndefinedError – if no information is available about the eigenvalues of the observable
 Returns
samples in an array of dimension
(shots,)
or counts Return type
Union[array[float], dict, list[dict]]

sample_basis_states
(number_of_states, state_probability)[source]¶ 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
state_probability (torch.Tensor[float]) – the computational basis probability vector
 Returns
the sampled basis states
 Return type
List[int]

shadow_expval
(obs, circuit)¶ Compute expectation values using classical shadows in a differentiable manner.
Please refer to
shadow_expval()
for detailed documentation. Parameters
obs (ClassicalShadowMP) – The classical shadow expectation value measurement process
circuit (QuantumTape) – The quantum tape that is being executed
 Returns
expectation value estimate.
 Return type
float

shot_vec_statistics
(circuit)¶ Process measurement results from circuit execution using a device with a shot vector and return statistics.
This is an auxiliary method of execute and uses statistics.
When using shot vectors, measurement results for each item of the shot vector are contained in a tuple.
 Parameters
circuit (QuantumTape) – circuit to execute on the device
 Raises
QuantumFunctionError – if the value of
return_type
is not supported Returns
stastics for each shot item from the shot vector
 Return type
tuple

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 (array[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
array[int]

statistics
(circuit, shot_range=None, bin_size=None)¶ Process measurement results from circuit execution and return statistics.
This includes returning expectation values, variance, samples, probabilities, states, and density matrices.
 Parameters
circuit (QuantumTape) – the quantum tape currently being executed
shot_range (tuple[int]) – 2tuple of integers specifying the range of samples to use. If not specified, all samples are used.
bin_size (int) – Divides the shot range into bins of size
bin_size
, and returns the measurement statistic separately over each bin. If not provided, the entire shot range is treated as a single bin.
 Raises
QuantumFunctionError – if the value of
return_type
is not supported Returns
the corresponding statistics
 Return type
Union[float, List[float]]
Usage Details
The
shot_range
andbin_size
arguments allow for the statistics to be performed on only a subset of device samples. This finer level of control is accessible from the main UI by instantiating a device with a batch of shots.For example, consider the following device:
>>> dev = qml.device("my_device", shots=[5, (10, 3), 100])
This device will execute QNodes using 135 shots, however measurement statistics will be course grained across these 135 shots:
All measurement statistics will first be computed using the first 5 shots — that is,
shots_range=[0, 5]
,bin_size=5
.Next, the tuple
(10, 3)
indicates 10 shots, repeated 3 times. We will want to useshot_range=[5, 35]
, performing the expectation value in bins of size 10 (bin_size=10
).Finally, we repeat the measurement statistics for the final 100 shots,
shot_range=[35, 135]
,bin_size=100
.

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
if supplied operation is supported Return type
bool

var
(observable, shot_range=None, bin_size=None)¶ 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

vn_entropy
(wires, log_base)¶ Returns the Von Neumann entropy prior to measurement.
\[S( \rho ) = \text{Tr}( \rho \log ( \rho ))\] Parameters
wires (Wires) – Wires of the considered subsystem.
log_base (float) – Base for the logarithm, default is None the natural logarithm is used in this case.
 Returns
returns the Von Neumann entropy
 Return type
float