RemoteDevice¶

class pennylane_qiskit.RemoteDevice(wires, backend, shots=1024, **kwargs)[source]¶

Bases: QiskitDevice

A PennyLane device for any Qiskit backend.

Parameters:

wires (int or 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 (['aux_wire', 'q1', 'q2']).
backend (Backend) – the initialized Qiskit backend

Keyword Arguments:

shots (Union[int, None]) – number of circuit evaluations/random samples used to estimate expectation values and variances of observables.
session (Session) – a Qiskit Session to use for device execution. If none is provided, a session will be created at each device execution.
compile_backend (Union[Backend, None]) – the backend to be used for compiling the circuit that will be sent to the backend device, to be set if the backend desired for compliation differs from the backend used for execution. Defaults to None, which means the primary backend will be used.
**kwargs – transpilation and runtime keyword arguments to be used for measurements with Primitives. If an options dictionary is defined amongst the kwargs, and there are settings that overlap with those in kwargs, the settings in options will take precedence over kwargs. Keyword arguments accepted by both the transpiler and at runtime (e.g. optimization_level) will be passed to the transpiler rather than to the Primitive.

Example:

import pennylane as qml
from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService(channel="ibm_quantum")
backend = service.least_busy(n_qubits=127, simulator=False, operational=True)
dev = qml.device("qiskit.remote", wires=127, backend=backend)

@qml.qnode(dev)
def circuit(x):
    qml.RX(x, wires=[0])
    qml.CNOT(wires=[0, 1])
    return qml.expval(qml.PauliZ(1))

>>> circuit(np.pi/3, shots=1024)
0.529296875

This device also supports the use of local simulators such as AerSimulator or fake backends such as FakeManila.

import pennylane as qml
from qiskit_aer import AerSimulator

backend = AerSimulator()
dev = qml.device("qiskit.remote", wires=5, backend=backend)

@qml.qnode(dev)
def circuit(x):
    qml.RX(x, wires=[0])
    qml.CNOT(wires=[0, 1])
    return qml.expval(qml.PauliZ(1))

>>> circuit(np.pi/3, shots=1024)
0.49755859375

We can also change the number of shots, either when initializing the device or when we execute the circuit. Note that the shots number specified on circuit execution will override whatever was set on device initialization.

dev = qml.device("qiskit.remote", wires=5, backend=backend, shots=2)

@qml.qnode(dev)
def circuit(x):
    qml.RX(x, wires=[0])
    qml.CNOT(wires=[0, 1])
    return qml.sample(qml.PauliZ(1))

>>> circuit(np.pi/3) # this will run with 2 shots
array([-1.,  1.])

>>> circuit(np.pi/3, shots=5) # this will run with 5 shots
array([-1., -1.,  1.,  1.,  1.])

>>> circuit(np.pi/3) # this will run with 2 shots
array([-1.,  1.])

Internally, the device uses the EstimatorV2 and the SamplerV2 runtime primitives to execute the measurements. To set options for transpilation or runtime, simply pass the keyword arguments into the device. If you wish to change options other than shots, PennyLane requires you to re-initialize the device to do so.

import pennylane as qml
from qiskit_ibm_runtime.fake_provider import FakeManilaV2

backend = FakeManilaV2()
dev = qml.device(
    "qiskit.remote",
    wires=5,
    backend=backend,
    resilience_level=1,
    optimization_level=1,
    seed_transpiler=42,
)
# to change options, re-initialize the device
dev = qml.device(
    "qiskit.remote",
    wires=5,
    backend=backend,
    resilience_level=1,
    optimization_level=2,
    seed_transpiler=24,
)

Attributes

`backend`	The Qiskit backend object.
`capabilities`	A `DeviceCapabilities` object describing the capabilities of the backend device.
`compile_backend`	The `compile_backend` is a Qiskit backend object to be used for transpilation.
`config_filepath`	A device can use a toml file to specify the capabilities of the backend device.
`name`	The name of the device or set of devices.
`num_wires`	Get the number of wires.
`observables`
`operations`
`service`	The QiskitRuntimeService service.
`session`	The QiskitRuntimeService session.
`short_name`
`shots`	Default shots for execution workflows containing this device.
`tracker`	A `Tracker` that can store information about device executions, shots, batches, intermediate results, or any additional device dependent information.
`wires`	The device wires.

backend¶

The Qiskit backend object.

Returns:: Qiskit backend object.
Return type:: qiskit.providers.Backend

capabilities: DeviceCapabilities | None = None¶: A DeviceCapabilities object describing the capabilities of the backend device.

compile_backend¶: The compile_backend is a Qiskit backend object to be used for transpilation. :returns: Qiskit backend object. :rtype: qiskit.providers.backend

config_filepath: str | None = None¶: A device can use a toml file to specify the capabilities of the backend device. If this is provided, the file will be loaded into a DeviceCapabilities object assigned to the capabilities attribute.

name¶

The name of the device or set of devices.

This property can either be the name of the class, or an alias to be used in the device() constructor, such as "default.qubit" or "lightning.qubit".

num_wires¶

Get the number of wires.

Returns:: The number of wires.
Return type:: int

observables = {'Hadamard', 'Hermitian', 'Identity', 'LinearCombination', 'PauliX', 'PauliY', 'PauliZ', 'Prod', 'Projector', 'SProd', 'Sum'}¶

operations = {'Adjoint(GlobalPhase)', 'Adjoint(S)', 'Adjoint(SX)', 'Adjoint(T)', 'Barrier', 'CCZ', 'CH', 'CNOT', 'CPhase', 'CRX', 'CRY', 'CRZ', 'CSWAP', 'CY', 'CZ', 'ECR', 'Hadamard', 'ISWAP', 'Identity', 'IsingXX', 'IsingYY', 'IsingZZ', 'PauliX', 'PauliY', 'PauliZ', 'PhaseShift', 'QubitUnitary', 'RX', 'RY', 'RZ', 'S', 'SWAP', 'SX', 'StatePrep', 'T', 'Toffoli', 'U1', 'U2', 'U3'}¶

service¶

The QiskitRuntimeService service.

Returns:: qiskit.qiskit_ibm_runtime.QiskitRuntimeService

session¶

The QiskitRuntimeService session.

Returns:: qiskit.qiskit_ibm_runtime.Session

short_name = 'qiskit.remote'¶

shots¶

Default shots for execution workflows containing this device.

Note that the device itself should always pull shots from the provided QuantumTape and its shots, not from this property. This property is used to provide a default at the start of a workflow.

tracker: Tracker = <pennylane.devices.tracker.Tracker object>¶

A Tracker that can store information about device executions, shots, batches, intermediate results, or any additional device dependent information.

A plugin developer can store information in the tracker by:

# querying if the tracker is active
if self.tracker.active:

    # store any keyword: value pairs of information
    self.tracker.update(executions=1, shots=self._shots, results=results)

    # Calling a user-provided callback function
    self.tracker.record()

wires¶

The device wires.

Note that wires are optional, and the default value of None means any wires can be used. If a device has wires defined, they will only be used for certain features. This includes:

Validation of tapes being executed on the device
Defining the wires used when evaluating a state() measurement

Methods

`compile_circuits`(circuits)	Compiles multiple circuits one after the other.
`compute_derivatives`(circuits[, ...])	Calculate the jacobian of either a single or a batch of circuits on the device.
`compute_jvp`(circuits, tangents[, ...])	The jacobian vector product used in forward mode calculation of derivatives.
`compute_vjp`(circuits, cotangents[, ...])	The vector jacobian product used in reverse-mode differentiation.
`eval_jaxpr`(jaxpr, consts, *args[, ...])	An experimental method for natively evaluating PLXPR.
`execute`(circuits[, execution_config])	Execute a circuit or a batch of circuits and turn it into results.
`execute_and_compute_derivatives`(circuits[, ...])	Compute the results and jacobians of circuits at the same time.
`execute_and_compute_jvp`(circuits, tangents)	Execute a batch of circuits and compute their jacobian vector products.
`execute_and_compute_vjp`(circuits, cotangents)	Calculate both the results and the vector jacobian product used in reverse-mode differentiation.
`generate_samples`([circuit])	Returns the computational basis samples generated for all wires.
`get_transpile_args`(kwargs)	The transpile argument setter.
`jaxpr_jvp`(jaxpr, args, tangents[, ...])	An experimental method for computing the results and jvp for PLXPR.
`observable_stopping_condition`(obs)	Specifies whether or not an observable is accepted by QiskitDevice2.
`preprocess`([execution_config])	This function defines the device transform program to be applied and an updated device configuration.
`preprocess_transforms`([execution_config])	Returns the transform program to preprocess a circuit for execution.
`reset`()	Reset the current job to None.
`setup_execution_config`([config, circuit])	Sets up an `ExecutionConfig` that configures the execution behaviour.
`stopping_condition`(op)	Specifies whether or not an Operator is accepted by QiskitDevice2.
`supports_derivatives`([execution_config, circuit])	Determine whether or not a device provided derivative is potentially available.
`supports_jvp`([execution_config, circuit])	Whether or not a given device defines a custom jacobian vector product.
`supports_vjp`([execution_config, circuit])	Whether or not a given device defines a custom vector jacobian product.
`update_session`(session)	Update the session attribute.

compile_circuits(circuits)¶

Compiles multiple circuits one after the other.

Parameters:: circuits (list[QuantumCircuit]) – the circuits to be compiled
Returns:: the list of compiled circuits
Return type:: list[QuantumCircuit]

compute_derivatives(circuits: ~pennylane.tape.qscript.QuantumScript | ~collections.abc.Sequence[~pennylane.tape.qscript.QuantumScript], execution_config: ~pennylane.devices.execution_config.ExecutionConfig = ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=<Interface.NUMPY: 'numpy'>, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None), convert_to_numpy=True, executor_backend=<class 'pennylane.concurrency.executors.native.multiproc.MPPoolExec'>))¶

Calculate the jacobian of either a single or a batch of circuits on the device.

Parameters:

circuits (Union[QuantumTape, Sequence[QuantumTape]]) – the circuits to calculate derivatives for
execution_config (ExecutionConfig) – a datastructure with all additional information required for execution

Returns:

The jacobian for each trainable parameter

Return type:

Tuple

Execution Config:

The execution config has gradient_method and order property that describes the order of differentiation requested. If the requested method or order of gradient is not provided, the device should raise a NotImplementedError. The supports_derivatives() method can pre-validate supported orders and gradient methods.

Return Shape:

If a batch of quantum scripts is provided, this method should return a tuple with each entry being the gradient of each individual quantum script. If the batch is of length 1, then the return tuple should still be of length 1, not squeezed.

compute_jvp(circuits: ~pennylane.tape.qscript.QuantumScript | ~collections.abc.Sequence[~pennylane.tape.qscript.QuantumScript], tangents: tuple[~numbers.Number, ...], execution_config: ~pennylane.devices.execution_config.ExecutionConfig = ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=<Interface.NUMPY: 'numpy'>, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None), convert_to_numpy=True, executor_backend=<class 'pennylane.concurrency.executors.native.multiproc.MPPoolExec'>))¶

The jacobian vector product used in forward mode calculation of derivatives.

Parameters:

circuits (Union[QuantumTape, Sequence[QuantumTape]]) – the circuit or batch of circuits
tangents (tensor-like) – Gradient vector for input parameters.
execution_config (ExecutionConfig) – a datastructure with all additional information required for execution

Returns:

A numeric result of computing the jacobian vector product

Return type:

Tuple

Definition of jvp:

If we have a function with jacobian:

\[\vec{y} = f(\vec{x}) \qquad J_{i,j} = \frac{\partial y_i}{\partial x_j}\]

The Jacobian vector product is the inner product with the derivatives of \(x\), yielding only the derivatives of the output \(y\):

\[\text{d}y_i = \Sigma_{j} J_{i,j} \text{d}x_j\]

Shape of tangents:

The tangents tuple should be the same length as circuit.get_parameters() and have a single number per parameter. If a number is zero, then the gradient with respect to that parameter does not need to be computed.

compute_vjp(circuits: ~pennylane.tape.qscript.QuantumScript | ~collections.abc.Sequence[~pennylane.tape.qscript.QuantumScript], cotangents: tuple[~numbers.Number, ...], execution_config: ~pennylane.devices.execution_config.ExecutionConfig = ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=<Interface.NUMPY: 'numpy'>, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None), convert_to_numpy=True, executor_backend=<class 'pennylane.concurrency.executors.native.multiproc.MPPoolExec'>))¶

The vector jacobian product used in reverse-mode differentiation.

Parameters:

circuits (Union[QuantumTape, Sequence[QuantumTape]]) – the circuit or batch of circuits
cotangents (Tuple[Number, Tuple[Number]]) – Gradient-output vector. Must have shape matching the output shape of the corresponding circuit. If the circuit has a single output, cotangents may be a single number, not an iterable of numbers.
execution_config (ExecutionConfig) – a datastructure with all additional information required for execution

Returns:

A numeric result of computing the vector jacobian product

Return type:

tensor-like

Definition of vjp:

If we have a function with jacobian:

\[\vec{y} = f(\vec{x}) \qquad J_{i,j} = \frac{\partial y_i}{\partial x_j}\]

The vector jacobian product is the inner product of the derivatives of the output y with the Jacobian matrix. The derivatives of the output vector are sometimes called the cotangents.

\[\text{d}x_i = \Sigma_{i} \text{d}y_i J_{i,j}\]

Shape of cotangents:

The value provided to cotangents should match the output of execute().

eval_jaxpr(jaxpr: jax.extend.core.Jaxpr, consts: list[TensorLike], *args, execution_config: ExecutionConfig | None = None) → list[TensorLike]¶

An experimental method for natively evaluating PLXPR. See the capture module for more details.

Parameters:

jaxpr (jax.extend.core.Jaxpr) – Pennylane variant jaxpr containing quantum operations and measurements
consts (list[TensorLike]) – the closure variables consts corresponding to the jaxpr
*args (TensorLike) – the variables to use with the jaxpr.

Keyword Arguments:

execution_config (Optional[ExecutionConfig]) – a data structure with additional information required for execution

Returns:

the result of evaluating the jaxpr with the given parameters.

Return type:

list[TensorLike]

execute(circuits: QuantumTape | Sequence[QuantumTape], execution_config: ExecutionConfig | None = None) → Result | Sequence[Result]¶: Execute a circuit or a batch of circuits and turn it into results.

execute_and_compute_derivatives(circuits: ~pennylane.tape.qscript.QuantumScript | ~collections.abc.Sequence[~pennylane.tape.qscript.QuantumScript], execution_config: ~pennylane.devices.execution_config.ExecutionConfig = ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=<Interface.NUMPY: 'numpy'>, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None), convert_to_numpy=True, executor_backend=<class 'pennylane.concurrency.executors.native.multiproc.MPPoolExec'>))¶

Compute the results and jacobians of circuits at the same time.

Parameters:

circuits (Union[QuantumTape, Sequence[QuantumTape]]) – the circuits or batch of circuits
execution_config (ExecutionConfig) – a datastructure with all additional information required for execution

Returns:

A numeric result of the computation and the gradient.

Return type:

tuple

See execute() and compute_derivatives() for more information about return shapes and behaviour. If compute_derivatives() is defined, this method should be as well.

This method can be used when the result and execution need to be computed at the same time, such as during a forward mode calculation of gradients. For certain gradient methods, such as adjoint diff gradients, calculating the result and gradient at the same can save computational work.

execute_and_compute_jvp(circuits: ~pennylane.tape.qscript.QuantumScript | ~collections.abc.Sequence[~pennylane.tape.qscript.QuantumScript], tangents: tuple[~numbers.Number, ...], execution_config: ~pennylane.devices.execution_config.ExecutionConfig = ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=<Interface.NUMPY: 'numpy'>, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None), convert_to_numpy=True, executor_backend=<class 'pennylane.concurrency.executors.native.multiproc.MPPoolExec'>))¶

Execute a batch of circuits and compute their jacobian vector products.

Parameters:

circuits (Union[QuantumTape, Sequence[QuantumTape]]) – circuit or batch of circuits
tangents (tensor-like) – Gradient vector for input parameters.
execution_config (ExecutionConfig) – a datastructure with all additional information required for execution

Returns:

A numeric result of execution and of computing the jacobian vector product

Return type:

Tuple, Tuple

See also

execute() and compute_jvp()

execute_and_compute_vjp(circuits: ~pennylane.tape.qscript.QuantumScript | ~collections.abc.Sequence[~pennylane.tape.qscript.QuantumScript], cotangents: tuple[~numbers.Number, ...], execution_config: ~pennylane.devices.execution_config.ExecutionConfig = ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=<Interface.NUMPY: 'numpy'>, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None), convert_to_numpy=True, executor_backend=<class 'pennylane.concurrency.executors.native.multiproc.MPPoolExec'>))¶

Calculate both the results and the vector jacobian product used in reverse-mode differentiation.

Parameters:

circuits (Union[QuantumTape, Sequence[QuantumTape]]) – the circuit or batch of circuits to be executed
cotangents (Tuple[Number, Tuple[Number]]) – Gradient-output vector. Must have shape matching the output shape of the corresponding circuit. If the circuit has a single output, cotangents may be a single number, not an iterable of numbers.
execution_config (ExecutionConfig) – a datastructure with all additional information required for execution

Returns:

the result of executing the scripts and the numeric result of computing the vector jacobian product

Return type:

Tuple, Tuple

See also

execute() and compute_vjp()

generate_samples(circuit=None)¶

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.

Parameters:: circuit (int) – position of the circuit in the batch.
Returns:: array of samples in the shape (dev.shots, dev.num_wires)
Return type:: array[complex]

static get_transpile_args(kwargs)¶

The transpile argument setter. This separates keyword arguments related to transpilation from the rest of the keyword arguments and removes those keyword arguments from kwargs.

Keyword Arguments:: kwargs (dict) – combined keyword arguments to be parsed for the Qiskit transpiler. For more details, see the Qiskit transpiler documentation
Returns:: keyword arguments for the runtime options and keyword arguments for the transpiler
Return type:: kwargs (dict), transpile_args (dict)

jaxpr_jvp(jaxpr: jax.extend.core.Jaxpr, args, tangents, execution_config: ExecutionConfig | None = None)¶

An experimental method for computing the results and jvp for PLXPR. See the capture module for more details.

Parameters:

jaxpr (jax.extend.core.Jaxpr) – Pennylane variant jaxpr containing quantum operations and measurements
args (Sequence[TensorLike]) – the consts followed by the normal arguments
tangents (Sequence[TensorLike]) – the tangents corresponding to args. May contain jax.interpreters.ad.Zero.

Keyword Arguments:

execution_config (Optional[ExecutionConfig]) – a data structure with additional information required for execution

Returns:

the results and jacobian vector products

Return type:

Sequence[TensorLike], Sequence[TensorLike]

>>> qml.capture.enable()
>>> import jax
>>> closure_var = jax.numpy.array(0.5)
>>> def f(x):
...     qml.RX(closure_var, 0)
...     qml.RX(x, 1)
...     return qml.expval(qml.Z(0)), qml.expval(qml.Z(1))
>>> jaxpr = jax.make_jaxpr(f)(1.2)
>>> args = (closure_var, 1.2)
>>> zero = jax.interpreters.ad.Zero(jax.core.ShapedArray((), float))
>>> tangents = (zero, 1.0)
>>> config = qml.devices.ExecutionConfig(gradient_method="adjoint")
>>> dev = qml.device('default.qubit', wires=2)
>>> res, jvps = dev.jaxpr_jvp(jaxpr.jaxpr, args, tangents, execution_config=config)
>>> res
[Array(0.87758255, dtype=float32), Array(0.36235774, dtype=float32)]
>>> jvps
[Array(0., dtype=float32), Array(-0.932039, dtype=float32)]

observable_stopping_condition(obs: Operator) → bool¶: Specifies whether or not an observable is accepted by QiskitDevice2.

preprocess(execution_config: ExecutionConfig | None = None) → Tuple[TransformProgram, ExecutionConfig]¶

This function defines the device transform program to be applied and an updated device configuration.

Parameters:: execution_config (Union[ExecutionConfig, Sequence[ExecutionConfig]]) – A data structure describing the parameters needed to fully describe the execution.
Returns:: A transform program that when called returns QuantumTapes that the device can natively execute as well as a postprocessing function to be called after execution, and a configuration with unset specifications filled in.
Return type:: TransformProgram, ExecutionConfig

This device:

Supports any operations with explicit PennyLane to Qiskit gate conversions defined in the plugin
Does not intrinsically support parameter broadcasting

preprocess_transforms(execution_config: ExecutionConfig | None = None) → TransformProgram¶

Returns the transform program to preprocess a circuit for execution.

Parameters:: execution_config (ExecutionConfig) – The execution configuration object
Returns:: A transform program that is called before execution
Return type:: TransformProgram

The transform program is composed of a list of individual transforms, which may include:

Decomposition of operations and measurements to what is supported by the device.
Splitting a circuit with measurements of non-commuting observables or Hamiltonians into multiple executions.
Splitting a circuit with batched parameters into multiple executions.
Validation of wires, measurements, and observables.
Gradient specific preprocessing, such as making sure trainable operators have generators.

Example

All transforms that are part of the preprocessing transform program need to respect the transform contract defined in pennylane.transform().

from pennylane.tape import QuantumScriptBatch
from pennylane.typing import PostprocessingFn

@qml.transform
def my_preprocessing_transform(tape: qml.tape.QuantumScript) -> tuple[QuantumScriptBatch, PostprocessingFn]:
    # e.g. valid the measurements, expand the tape for the hardware execution, ...

    def blank_processing_fn(results):
        return results[0]

    return [tape], processing_fn

A transform program can hold an arbitrary number of individual transforms:

def preprocess(self, config):
    program = TransformProgram()
    program.add_transform(my_preprocessing_transform)
    return program

See also

transform() and TransformProgram

Post processing function and derivatives

Derivatives and Jacobian products will be bound to the machine learning library before the postprocessing function is called on the results. Therefore, the machine learning library will be responsible for combining and post-processing derivatives returned from the device.

from pennylane.interfaces.jax import execute as jax_boundary

def f(x):
    circuit = qml.tape.QuantumScript([qml.Rot(*x, wires=0)], [qml.expval(qml.Z(0))])
    config = ExecutionConfig(gradient_method="adjoint")
    config = dev.setup_execution_config(config)
    program = dev.preprocess_transforms(config)
    circuit_batch, postprocessing = program((circuit, ))

    def execute_fn(tapes):
        return dev.execute_and_compute_derivatives(tapes, config)

    results = jax_boundary(circuit_batch, dev, execute_fn, None, {})
    return postprocessing(results)

x = jax.numpy.array([1.0, 2.0, 3.0])
jax.grad(f)(x)

In the above code, the quantum derivatives are registered with jax in the jax_boundary function. Only then is the classical postprocessing called on the result object.

reset()¶: Reset the current job to None.

setup_execution_config(config: ExecutionConfig | None = None, circuit: QuantumScript | None = None) → ExecutionConfig¶

Sets up an ExecutionConfig that configures the execution behaviour.

The execution config stores information on how the device should perform the execution, as well as how PennyLane should interact with the device. See ExecutionConfig for all available options and what they mean.

An ExecutionConfig is constructed from arguments passed to the QNode, and this method allows the device to update the config object based on device-specific requirements or preferences. See Execution Config for more details.

Parameters:

config (ExecutionConfig) – The initial ExecutionConfig object that describes the parameters needed to configure the execution behaviour.
circuit (QuantumScript) – The quantum circuit to customize the execution config for.

Returns:

The updated ExecutionConfig object

Return type:

ExecutionConfig

stopping_condition(op: Operator) → bool¶: Specifies whether or not an Operator is accepted by QiskitDevice2.

supports_derivatives(execution_config: ExecutionConfig | None = None, circuit: QuantumScript | None = None) → bool¶

Determine whether or not a device provided derivative is potentially available.

Default behaviour assumes first order device derivatives for all circuits exist if compute_derivatives() is overriden.

Parameters:

execution_config (ExecutionConfig) – A description of the hyperparameters for the desired computation.
circuit (None, QuantumTape) – A specific circuit to check differentation for.

Returns:

Bool

The device can support multiple different types of “device derivatives”, chosen via execution_config.gradient_method. For example, a device can natively calculate "parameter-shift" derivatives, in which case compute_derivatives() will be called for the derivative instead of execute() with a batch of circuits.

>>> config = ExecutionConfig(gradient_method="parameter-shift")
>>> custom_device.supports_derivatives(config)
True

In this case, compute_derivatives() or execute_and_compute_derivatives() will be called instead of execute() with a batch of circuits.

If circuit is not provided, then the method should return whether or not device derivatives exist for any circuit.

Example:

For example, the Python device will support device differentiation via the adjoint differentiation algorithm if the order is 1 and the execution occurs with no shots (shots=None).

>>> config = ExecutionConfig(derivative_order=1, gradient_method="adjoint")
>>> dev.supports_derivatives(config)
True
>>> circuit_analytic = qml.tape.QuantumScript([qml.RX(0.1, wires=0)], [qml.expval(qml.Z(0))], shots=None)
>>> dev.supports_derivatives(config, circuit=circuit_analytic)
True
>>> circuit_finite_shots = qml.tape.QuantumScript([qml.RX(0.1, wires=0)], [qml.expval(qml.Z(0))], shots=10)
>>> dev.supports_derivatives(config, circuit = circuit_fintite_shots)
False

>>> config = ExecutionConfig(derivative_order=2, gradient_method="adjoint")
>>> dev.supports_derivatives(config)
False

Adjoint differentiation will only be supported for circuits with expectation value measurements. If a circuit is provided and it cannot be converted to a form supported by differentiation method by preprocess(), then supports_derivatives should return False.

>>> config = ExecutionConfig(derivative_order=1, shots=None, gradient_method="adjoint")
>>> circuit = qml.tape.QuantumScript([qml.RX(2.0, wires=0)], [qml.probs(wires=(0,1))])
>>> dev.supports_derivatives(config, circuit=circuit)
False

If the circuit is not natively supported by the differentiation method but can be converted into a form that is supported, it should still return True. For example, Rot gates are not natively supported by adjoint differentation, as they do not have a generator, but they can be compiled into operations supported by adjoint differentiation. Therefore this method may reproduce compilation and validation steps performed by preprocess().

>>> config = ExecutionConfig(derivative_order=1, shots=None, gradient_method="adjoint")
>>> circuit = qml.tape.QuantumScript([qml.Rot(1.2, 2.3, 3.4, wires=0)], [qml.expval(qml.Z(0))])
>>> dev.supports_derivatives(config, circuit=circuit)
True

Backpropagation:

This method is also used be to validate support for backpropagation derivatives. Backpropagation is only supported if the device is transparent to the machine learning framework from start to finish.

>>> config = ExecutionConfig(gradient_method="backprop")
>>> python_device.supports_derivatives(config)
True
>>> cpp_device.supports_derivatives(config)
False

supports_jvp(execution_config: ExecutionConfig | None = None, circuit: QuantumScript | None = None) → bool¶

Whether or not a given device defines a custom jacobian vector product.

Parameters:

execution_config (ExecutionConfig) – A description of the hyperparameters for the desired computation.
circuit (None, QuantumTape) – A specific circuit to check differentation for.

Default behaviour assumes this to be True if compute_jvp() is overridden.

supports_vjp(execution_config: ExecutionConfig | None = None, circuit: QuantumScript | None = None) → bool¶

Whether or not a given device defines a custom vector jacobian product.

Parameters:

execution_config (ExecutionConfig) – A description of the hyperparameters for the desired computation.
circuit (None, QuantumTape) – A specific circuit to check differentation for.

Default behaviour assumes this to be True if compute_vjp() is overridden.

update_session(session)¶

Update the session attribute.

Parameters:: session – The new session to be set.

code/api/pennylane_qiskit.RemoteDevice

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RemoteDevice¶

Attributes

Methods

Post processing function and derivatives

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RemoteDevice¶

Attributes

Methods

Post processing function and derivatives

Contents

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