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']). Default None 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}

NullQubit tracks:

name

The name of the device.

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.

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 its shots, 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 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

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 reverse-mode 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 reverse-mode 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

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, 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 (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, 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 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().

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 data-types like torch.Tensor or jax.Array when gradient_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.

See Return Type Specification for more detailed information.

The result for each QuantumTape must match the shape specified by shape.

The level of priority for dimensions from outer dimension to inner dimension is:

  1. Quantum Script in batch

  2. Shot choice in a shot vector

  3. Measurement in the quantum script

  4. Parameter broadcasting

  5. Measurement shape for array-valued 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 script

  • result[0][0]: the first shot number in the shot vector

  • result[0][0][0]: the first measurement in the quantum script

  • result[0][0][0][0]: the first parameter broadcasting choice

  • result[0][0][0][0][0]: the first value for an array-valued 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 an ExecutionConfig 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() 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, 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 (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, 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 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()

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

TransformProgram, ExecutionConfig

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 and MeasurementProcess objects supported by the device.

  • splitting a circuit with the measurement of non-commuting 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() and TransformProgram

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 "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=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 if compute_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 if compute_vjp() is overridden.