qml.devices.Device

class Device(wires=None, shots=None)[source]

Bases: abc.ABC

A device driver that can control one or more backends. A backend can be either a physical Quantum Processing Unit or a virtual one such as a simulator.

Only the execute method must be defined to construct a device driver.

Streamlined interface: Only methods that are required to interact with the rest of PennyLane will be placed in the interface. Developers will be able to clearly see what they can change while still having a fully functional device.

Reduction of duplicate methods: Methods that solve similar problems are combined together. Only one place will have to solve each individual problem.

Support for dynamic execution configurations: Properties such as shots belong to specific executions.

Greater coverage for differentiation methods: Devices can define any order of derivative, the vector jacobian product, or the jacobian vector product. Calculation of derivatives can be done at the same time as execution to allow reuse of intermediate results.

pennylane.Device.batch_execute() and execute() are now a single method, execute()

batch_transform() and expand_fn() are now a single method, preprocess()

Shot information is no longer stored on the device, but instead specified on individual input QuantumTape.

The old devices defined a capabilities() dictionary that defined characteristics of the devices and controlled various preprocessing and validation steps, such as "supports_broadcasting". These capabilites should now be handled by the preprocess() method. For example, if a device does not support broadcasting, preprocess should split a quantum script with broadcasted parameters into a batch of quantum scripts. If the device does not support mid circuit measurements, then preprocess should apply defer_measurements(). A set of default preprocessing steps will be available to make a seamless transition to the new interface.

A class will be provided to easily construct default preprocessing steps from supported operations, supported observables, supported measurement processes, and various capabilities.

Utility functions will be added to the devices module to query whether or not the device driver can do certain things, such as devices.supports_operator(op, dev, native=True). These functions will work by checking the behaviour of preprocess() to certain inputs.

Versioning should be specified by the package containing the device. If an external package includes a PennyLane device, then the package requirements should specify the minimium PennyLane version required to work with the device.

The preprocess() method is assumed to be run before any execute() or differentiation method. If an arbitrary, non-preprocessed circuit is provided, execute() has no responsibility to perform any validation or provide clearer error messages.

>>> op = qml.Permute(["c", 3,"a",2,0], wires=[3,2,"a",0,"c"])
>>> circuit = qml.tape.QuantumScript([op], [qml.state()])
>>> dev = DefaultQubit()
>>> dev.execute(circuit)
MatrixUndefinedError
>>> circuit = qml.tape.QuantumScript([qml.Rot(1.2, 2.3, 3.4, 0)], [qml.expval(qml.PauliZ(0))])
>>> config = ExecutionConfig(gradient_method="adjoint")
>>> dev.compute_derivatives(circuit, config)
ValueError: Operation Rot is not written in terms of a single parameter
>>> new_circuit, postprocessing, new_config = dev.preprocess(circuit, config)
>>> dev.compute_derivatives(new_circuit, new_config)
((array(0.), array(-0.74570521), array(0.)),)

Any validation checks or error messages should occur in preprocess() to avoid failures after expending computation resources.

Execution config properties related to configuring a device include:

  • device_options: A dictionary of device specific options. For example, the python device may have multiprocessing_mode as a key. These should be documented in the class docstring.

  • gradient_method: A device can choose to have native support for any type of gradient method. If the method supports_derivatives() returns True for a particular gradient method, it will be treated as a device derivative and not handled by pennylane core code.

  • gradient_keyword_arguments: Options for the gradient method.

  • derivative_order: Relevant for requested device derivatives.

name

The name of the device or set of devices.

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 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".

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))[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))[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))[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().

abstract 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))[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.

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.PauliZ(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.PauliZ(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))[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))[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))[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))[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.PauliZ(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.PauliZ(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.PauliZ(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.PauliZ(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.