qml.devices.NullQubit¶

class
NullQubit
(wires=None, shots=None)[source]¶ Bases:
pennylane.devices.device_api.Device
Null qubit device for PennyLane. This device performs no operations involved in numerical calculations. Instead the time spent in execution is dominated by support (or setting up) operations, like tape creation etc.
 Parameters
wires (int, Iterable[Number, str]) – Number of wires present on the device, or iterable that contains unique labels for the wires as numbers (i.e.,
[1, 0, 2]
) or strings (['aux_wire', 'q1', 'q2']
). DefaultNone
if not specified.shots (int, Sequence[int], Sequence[Union[int, Sequence[int]]]) – The default number of shots to use in executions involving this device.
Example:
qs = qml.tape.QuantumScript( [qml.Hadamard(0), qml.CNOT([0, 1])], [qml.expval(qml.PauliZ(0)), qml.probs()], ) qscripts = [qs, qs, qs]
>>> dev = NullQubit() >>> program, execution_config = dev.preprocess() >>> new_batch, post_processing_fn = program(qscripts) >>> results = dev.execute(new_batch, execution_config=execution_config) >>> post_processing_fn(results) ((array(0.), array([1., 0., 0., 0.])), (array(0.), array([1., 0., 0., 0.])), (array(0.), array([1., 0., 0., 0.])))
This device currently supports (trivial) derivatives:
>>> from pennylane.devices import ExecutionConfig >>> dev.supports_derivatives(ExecutionConfig(gradient_method="device")) True
This device can be used to track resource usage:
n_layers = 50 n_wires = 100 shape = qml.StronglyEntanglingLayers.shape(n_layers=n_layers, n_wires=n_wires) @qml.qnode(dev) def circuit(params): qml.StronglyEntanglingLayers(params, wires=range(n_wires)) return [qml.expval(qml.Z(i)) for i in range(n_wires)] params = np.random.random(shape) with qml.Tracker(dev) as tracker: circuit(params)
>>> tracker.history["resources"][0] wires: 100 gates: 10000 depth: 502 shots: Shots(total=None) gate_types: {'Rot': 5000, 'CNOT': 5000} gate_sizes: {1: 5000, 2: 5000}
Tracking
NullQubit
tracks:executions
: the number of unique circuits that would be required on quantum hardwareshots
: the number of shotsresources
: theResources
for the executed circuit.simulations
: the number of simulations performed. One simulation can cover multiple QPU executions, such as for noncommuting measurements and batched parameters.batches
: The number of timesexecute()
is called.results
: The results of each call ofexecute()
derivative_batches
: How many timescompute_derivatives()
is called.execute_and_derivative_batches
: How many timesexecute_and_compute_derivatives()
is calledvjp_batches
: How many timescompute_vjp()
is calledexecute_and_vjp_batches
: How many timesexecute_and_compute_vjp()
is calledjvp_batches
: How many timescompute_jvp()
is calledexecute_and_jvp_batches
: How many timesexecute_and_compute_jvp()
is calledderivatives
: How many circuits are submitted tocompute_derivatives()
orexecute_and_compute_derivatives()
.vjps
: How many circuits are submitted tocompute_vjp()
orexecute_and_compute_vjp()
jvps
: How many circuits are submitted tocompute_jvp()
orexecute_and_compute_jvp()
Attributes
The name of the device.
Default shots for execution workflows containing this device.
A
Tracker
that can store information about device executions, shots, batches, intermediate results, or any additional device dependent information.The device wires.

name
¶ The name of the device.

shots
¶ Default shots for execution workflows containing this device.
Note that the device itself should always pull shots from the provided
QuantumTape
and itsshots
, not from this property. This property is used to provide a default at the start of a workflow.

tracker
: pennylane.tracker.Tracker = <pennylane.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 userprovided 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
compute_derivatives
(circuits[, execution_config])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 reversemode differentiation.
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 reversemode differentiation.
preprocess
([execution_config])Device preprocessing function.
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.

compute_derivatives
(circuits, execution_config=ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None)))[source]¶ 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
See also
supports_derivatives()
andexecute_and_compute_derivatives()
.Execution Config:
The execution config has
gradient_method
andorder
property that describes the order of differentiation requested. If the requested method or order of gradient is not provided, the device should raise aNotImplementedError
. Thesupports_derivatives()
method can prevalidate 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, tangents, execution_config=ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None)))[source]¶ The jacobian vector product used in forward mode calculation of derivatives.
 Parameters
circuits (Union[QuantumTape, Sequence[QuantumTape]]) – the circuit or batch of circuits
tangents (tensorlike) – 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 ascircuit.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, cotangents, execution_config=ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None)))[source]¶ The vector jacobian product used in reversemode differentiation.
 Parameters
circuits (Union[QuantumTape, Sequence[QuantumTape]]) – the circuit or batch of circuits
cotangents (Tuple[Number, Tuple[Number]]) – Gradientoutput 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
tensorlike
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 ofexecute()
.

execute
(circuits, execution_config=ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None)))[source]¶ Execute a circuit or a batch of circuits and turn it into results.
 Parameters
circuits (Union[QuantumTape, Sequence[QuantumTape]]) – the quantum circuits to be executed
execution_config (ExecutionConfig) – a datastructure with additional information required for execution
 Returns
A numeric result of the computation.
 Return type
TensorLike, tuple[TensorLike], tuple[tuple[TensorLike]]
Interface parameters:
The provided
circuits
may contain interface specific datatypes liketorch.Tensor
orjax.Array
whengradient_method
of"backprop"
is requested. If the gradient method is not backpropagation, then only vanilla numpy parameters or builtins will be present in the circuits.Return Shape
See Return Type Specification for more detailed information.
The result for each
QuantumTape
must match the shape specified byshape
.The level of priority for dimensions from outer dimension to inner dimension is:
Quantum Script in batch
Shot choice in a shot vector
Measurement in the quantum script
Parameter broadcasting
Measurement shape for arrayvalued measurements like probabilities
For a batch of quantum scripts with multiple measurements, a shot vector, and parameter broadcasting:
result[0]
: the results for the first scriptresult[0][0]
: the first shot number in the shot vectorresult[0][0][0]
: the first measurement in the quantum scriptresult[0][0][0][0]
: the first parameter broadcasting choiceresult[0][0][0][0][0]
: the first value for an arrayvalued measurement
With the exception of quantum script batches, dimensions with only a single component should be eliminated.
For example:
With a single script and a single measurement process, execute should return just the measurement value in a numpy array.
shape
currently accepts a device, as historically devices stored shot information. In the future, this method will accept anExecutionConfig
instead.>>> tape = qml.tape.QuantumTape(measurements=qml.expval(qml.Z(0))]) >>> tape.shape(dev) () >>> dev.execute(tape) array(1.0)
If execute recieves a batch of scripts, then it should return a tuple of results:
>>> dev.execute([tape, tape]) (array(1.0), array(1.0)) >>> dev.execute([tape]) (array(1.0),)
If the script has multiple measurments, then the device should return a tuple of measurements.
>>> tape = qml.tape.QuantumTape(measurements=[qml.expval(qml.Z(0)), qml.probs(wires=(0,1))]) >>> tape.shape(dev) ((), (4,)) >>> dev.execute(tape) (array(1.0), array([1., 0., 0., 0.]))

execute_and_compute_derivatives
(circuits, execution_config=ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None)))[source]¶ 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()
andcompute_derivatives()
for more information about return shapes and behaviour. Ifcompute_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, tangents, execution_config=ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None)))[source]¶ Execute a batch of circuits and compute their jacobian vector products.
 Parameters
circuits (Union[QuantumTape, Sequence[QuantumTape]]) – circuit or batch of circuits
tangents (tensorlike) – 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_vjp
(circuits, cotangents, execution_config=ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None)))[source]¶ Calculate both the results and the vector jacobian product used in reversemode differentiation.
 Parameters
circuits (Union[QuantumTape, Sequence[QuantumTape]]) – the circuit or batch of circuits to be executed
cotangents (Tuple[Number, Tuple[Number]]) – Gradientoutput 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

preprocess
(execution_config=ExecutionConfig(grad_on_execution=None, use_device_gradient=None, use_device_jacobian_product=None, gradient_method=None, gradient_keyword_arguments={}, device_options={}, interface=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None)))[source]¶ Device preprocessing function.
Warning
This function is tracked by machine learning interfaces and should be fully differentiable. The
pennylane.math
module can be used to construct fully differentiable transformations.Additional preprocessing independent of machine learning interfaces can be done inside of the
execute()
method. Parameters
execution_config (ExecutionConfig) – A datastructure describing the parameters needed to fully describe the execution.
 Returns
 A transform program that is called before execution, and a configuration
with unset specifications filled in.
 Return type
 Raises
Exception – An exception can be raised if the input cannot be converted into a form supported by the device.
Preprocessing program may include:
expansion to
Operator
’s andMeasurementProcess
objects supported by the device.splitting a circuit with the measurement of noncommuting observables or Hamiltonians into multiple executions
splitting circuits with batched parameters into multiple executions
gradient specific preprocessing, such as making sure trainable operators have generators
validation of configuration parameters
choosing a best gradient method and
grad_on_execution
value.
Example
All the transforms that are part of the preprocessing need to respect the transform contract defined in
pennylane.transform()
.@transform def my_preprocessing_transform(tape: qml.tape.QuantumTape) > (Sequence[qml.tape.QuantumTape], callable): # e.g. valid the measurements, expand the tape for the hardware execution, ... def blank_processing_fn(results): return results[0] return [tape], processing_fn
Then we can define the preprocess method on the custom device. The program can accept an arbitrary number of transforms.
def preprocess(config): program = TransformProgram() program.add_transform(my_preprocessing_transform) return program, config
See also
transform()
andTransformProgram
Post processing function and derivatives
Derivatives and jacobian products will be bound to the machine learning library before the postprocessing function is called on results. Therefore the machine learning library will be responsible for combining the device provided derivatives and post processing derivatives.
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") program, config = dev.preprocess(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.

supports_derivatives
(execution_config=None, circuit=None)[source]¶ 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"parametershift"
derivatives, in which casecompute_derivatives()
will be called for the derivative instead ofexecute()
with a batch of circuits.>>> config = ExecutionConfig(gradient_method="parametershift") >>> custom_device.supports_derivatives(config) True
In this case,
compute_derivatives()
orexecute_and_compute_derivatives()
will be called instead ofexecute()
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()
, thensupports_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 bypreprocess()
.>>> 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=None, circuit=None)[source]¶ 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
ifcompute_jvp()
is overridden.

supports_vjp
(execution_config=None, circuit=None)[source]¶ 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
ifcompute_vjp()
is overridden.