QPUDevice¶
- class QPUDevice(wires, *, gateset='qis', shots=1024, backend='harmony', error_mitigation=None, sharpen=None, api_key=None)[source]¶
Bases:
IonQDevice
QPU device for IonQ.
- Parameters:
wires (int or Iterable[Number, str]]) – Number of wires to initialize the device with, or iterable that contains unique labels for the subsystems as numbers (i.e.,
[-1, 0, 2]
) or strings (['ancilla', 'q1', 'q2']
).gateset (str) – the target gateset, either
"qis"
or"native"
. Defaults toqis
.backend (str) – Optional specifier for an IonQ backend. Can be
"harmony"
,"aria-1"
, etc. Default toharmony
.shots (int, list[int]) – Number of circuit evaluations/random samples used to estimate expectation values of observables. Defaults to 1024. If a list of integers is passed, the circuit evaluations are batched over the list of shots.
api_key (str) – The IonQ API key. If not provided, the environment variable
IONQ_API_KEY
is used.error_mitigation (dict) – settings for error mitigation when creating a job. Defaults to None. Not available on all backends. Set by default on some hardware systems. See IonQ API Job Creation and IonQ Debiasing and Sharpening for details. Valid keys include:
debias
(bool).sharpen (bool) –
whether to use sharpening when accessing the results of an executed job. Defaults to None (no value passed at job retrieval). Will generally return more accurate results if your expected output distribution has peaks. See IonQ Debiasing and Sharpening for details.
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.
Get the supported set of operations.
Mapping from free parameter index to the list of
Operations
in the device queue that depend on it.Array of computational basis state probabilities.
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 user-provided 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.
- author = 'Xanadu Inc.'¶
- 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]): 2-tuple 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 a device that inherits from
DefaultQubitLegacy
and overrides the logic of the qml.sample measurement. To do so we will need to update themeasurement_map
dictionary:class NewDevice(DefaultQubitLegacy): 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 = 'IonQ QPU 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', 'Identity', 'PauliX', 'PauliY', 'PauliZ', 'Prod'}¶
- 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 = '>=0.15.0'¶
- prob¶
Array of computational basis state probabilities. If no job has been submitted, returns
None
.- Type:
None or array[float]
- short_name = 'ionq.qpu'¶
- 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.legacy", 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
- 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.
- 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 = '0.37.0-dev'¶
- 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.
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, **kwargs)Apply quantum operations, rotate the circuit into the measurement basis, and compile and execute the quantum circuit.
batch_execute
(circuits, **kwargs)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 user-provided 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 observable on specified wires.
generate_basis_states
(num_wires[, dtype])Generates basis states in binary representation according to the number of wires specified.
Generates samples from the qpu.
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 non-specified 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:
Wires
- adjoint_jacobian(tape: QuantumScript, 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.
Cannot differentiate with respect to state-prep operations.
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) – post-forward pass state to start execution with. It should be complex-valued. 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 multi-parameter 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 \(|q_0,q_1,\dots,q_{N-1}\rangle\) 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:
array[float]
- apply(operations, **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 pre-measurement into the eigenbasis of the observables.
hash (int) – the hash value of the circuit constructed by CircuitGraph.hash
- batch_execute(circuits, **kwargs)¶
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[QuantumTape]) – circuits to execute on the device
- Returns:
list of measured value(s)
- Return type:
list[array[float]]
- batch_transform(circuit: QuantumScript)¶
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()¶
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 is device-agnostic and works by executing single-shot tapes containing randomized Pauli observables. Devices should override this if they can offer cleaner or faster implementations.
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.legacy", 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:
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]))])
- 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]) – 2-tuple 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)¶
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 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 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()
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:
.QuantumTape
- expval(observable, shot_range=None, bin_size=None)¶
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:
array[int]
- generate_samples()[source]¶
Generates samples from the qpu.
Note that the order of the samples returned here is not indicative of the order in which the experiments were done, but is instead controlled by a random shuffle (and hence set by numpy random seed).
- 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
- 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 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.
- Parameters:
wires0 (Wires) – wires of the first subsystem
wires1 (Wires) – wires of the second subsystem
log_base (float) – base to use in the logarithm
- Returns:
the mutual information
- Return type:
float
- 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)
- 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]) – 2-tuple 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)¶
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 (array[float]) – the computational basis probability vector
- Returns:
the sampled basis states
- Return type:
array[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: QuantumScript)¶
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:
statistics 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: QuantumScript, 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]) – 2-tuple 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