LightningGPU¶

class LightningGPU(wires: ~typing.Union[int, ~typing.List], *, c_dtype: ~typing.Union[~numpy.complex128, ~numpy.complex64] = <class 'numpy.complex128'>, shots: ~typing.Optional[~typing.Union[int, ~typing.List]] = None, batch_obs: bool = False, mpi: bool = False, mpi_buf_size: int = 0, use_async: bool = False)[source]¶

Bases: LightningBase

PennyLane Lightning GPU device.

A device that interfaces with C++ to perform fast linear algebra calculations.

Use of this device requires pre-built binaries or compilation from source. Check out the Lightning-GPU installation guide for more details.

Parameters

wires (int) – the number of wires to initialize the device with
c_dtype – Datatypes for statevector representation. Must be one of np.complex64 or np.complex128.
shots (int) – How many times the circuit should be evaluated (or sampled) to estimate the expectation values. Defaults to None if not specified. Setting to None results in computing statistics like expectation values and variances analytically.
batch_obs (bool) – Determine whether we process observables in parallel when computing the jacobian. This value is only relevant when the lightning.gpu is built with MPI. Default is False.
mpi (bool) – declare if the device will use the MPI support.
mpi_buf_size (int) – size of GPU memory (in MiB) set for MPI operation and its default value is 64 MiB.
use_async (bool) – is host-device data copy asynchronized or not.

Attributes

`c_dtype`	State vector complex data type.
`capabilities`	A `DeviceCapabilities` object describing the capabilities of the backend device.
`config_filepath`	A device can use a toml file to specify the capabilities of the backend device.
`dtype`	State vector complex data type.
`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.

c_dtype¶: State vector complex data type.

capabilities: Optional[DeviceCapabilities] = DeviceCapabilities(operations={'Identity': OperatorProperties(invertible=True, controllable=False, differentiable=True, conditions=[]), 'PauliX': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'PauliY': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'PauliZ': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'Hadamard': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'S': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'T': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'PhaseShift': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'RX': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'RY': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'RZ': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'Rot': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'CNOT': OperatorProperties(invertible=True, controllable=False, differentiable=True, conditions=[]), 'CY': OperatorProperties(invertible=True, controllable=False, differentiable=True, conditions=[]), 'CZ': OperatorProperties(invertible=True, controllable=False, differentiable=True, conditions=[]), 'SWAP': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'CSWAP': OperatorProperties(invertible=True, controllable=False, differentiable=True, conditions=[]), 'Toffoli': OperatorProperties(invertible=True, controllable=False, differentiable=True, conditions=[]), 'IsingXX': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'IsingXY': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'IsingYY': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'IsingZZ': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'ControlledPhaseShift': OperatorProperties(invertible=True, controllable=False, differentiable=True, conditions=[]), 'CRX': OperatorProperties(invertible=True, controllable=False, differentiable=True, conditions=[]), 'CRY': OperatorProperties(invertible=True, controllable=False, differentiable=True, conditions=[]), 'CRZ': OperatorProperties(invertible=True, controllable=False, differentiable=True, conditions=[]), 'CRot': OperatorProperties(invertible=True, controllable=False, differentiable=False, conditions=[]), 'SingleExcitation': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'SingleExcitationPlus': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'SingleExcitationMinus': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'DoubleExcitation': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'DoubleExcitationPlus': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'DoubleExcitationMinus': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'MultiRZ': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'QubitUnitary': OperatorProperties(invertible=True, controllable=True, differentiable=False, conditions=[]), 'GlobalPhase': OperatorProperties(invertible=True, controllable=True, differentiable=True, conditions=[]), 'BlockEncode': OperatorProperties(invertible=False, controllable=True, differentiable=False, conditions=[]), 'ControlledQubitUnitary': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[]), 'ECR': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[]), 'SX': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[]), 'ISWAP': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[]), 'PSWAP': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[]), 'SISWAP': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[]), 'SQISW': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[]), 'OrbitalRotation': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[]), 'QubitCarry': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[]), 'QubitSum': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[]), 'DiagonalQubitUnitary': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[]), 'MultiControlledX': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[])}, observables={'Identity': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'PauliX': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'PauliY': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'PauliZ': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'Hadamard': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'Hermitian': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'SparseHamiltonian': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'Sum': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'SProd': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'Prod': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'Exp': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'LinearCombination': OperatorProperties(invertible=False, controllable=False, differentiable=True, conditions=[]), 'Projector': OperatorProperties(invertible=False, controllable=False, differentiable=False, conditions=[])}, measurement_processes={'ExpectationMP': [], 'VarianceMP': [], 'ProbabilityMP': [], 'StateMP': [<ExecutionCondition.ANALYTIC_MODE_ONLY: 'analytic'>], 'SampleMP': [<ExecutionCondition.FINITE_SHOTS_ONLY: 'finiteshots'>], 'CountsMP': [<ExecutionCondition.FINITE_SHOTS_ONLY: 'finiteshots'>]}, qjit_compatible=True, runtime_code_generation=False, dynamic_qubit_management=False, overlapping_observables=True, non_commuting_observables=True, initial_state_prep=True, supported_mcm_methods=['one-shot'])¶: A DeviceCapabilities object describing the capabilities of the backend device.

config_filepath: Optional[str] = PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/xanaduai-pennylane-lightning/envs/latest/lib/python3.10/site-packages/pennylane_lightning/lightning_gpu/lightning_gpu.toml')¶: 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.

dtype¶: State vector complex data type.

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: 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

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 reverse-mode differentiation. `Lightning[Device]` uses the adjoint differentiation method to compute the VJP. :param circuits: the circuit or batch of circuits :type circuits: Union[QuantumTape, Sequence[QuantumTape]] :param cotangents: 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. :type cotangents: Tuple[Number, Tuple[Number]] :param execution_config: a datastructure with all additional information required for execution :type execution_config: ExecutionConfig.
`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. :param circuits: the circuit or batch of circuits to be executed :type circuits: Union[QuantumTape, Sequence[QuantumTape]] :param cotangents: 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. :type cotangents: Tuple[Number, Tuple[Number]] :param execution_config: a datastructure with all additional information required for execution :type execution_config: ExecutionConfig.
`get_c_interface`()	Returns a tuple consisting of the device name, and the location to the shared object with the C/C++ device implementation.
`jacobian`(circuit, state[, batch_obs, wire_map])	Compute the Jacobian for a single quantum script.
`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.
`setup_execution_config`([config])	Sets up an `ExecutionConfig` that configures the execution behaviour.
`simulate`(circuit, state[, postselect_mode])	Simulate a single quantum script.
`simulate_and_jacobian`(circuit, state[, ...])	Simulate a single quantum script and compute its Jacobian.
`simulate_and_vjp`(circuit, cotangents, state)	Simulate a single quantum script and compute its Vector-Jacobian Product (VJP). :param circuit: The single circuit to simulate :type circuit: QuantumTape :param cotangents: 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. :type cotangents: Tuple[Number, Tuple[Number]] :param state: handle to the Lightning state vector :type state: Lightning [Device] StateVector :param batch_obs: Determine whether we process observables in parallel when computing the jacobian. :type batch_obs: bool :param wire_map: a map from wire labels to simulation indices :type wire_map: Optional[dict].
`supports_derivatives`([execution_config, circuit])	Check whether or not derivatives are available for a given configuration and circuit.
`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 this device defines a custom vector jacobian product.
`vjp`(circuit, cotangents, state[, batch_obs, ...])	Compute the Vector-Jacobian Product (VJP) for a single quantum script. :param circuit: The single circuit to simulate :type circuit: QuantumTape :param cotangents: 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. :type cotangents: Tuple[Number, Tuple[Number]] :param state: handle to the Lightning state vector :type state: Lightning [Device] StateVector :param batch_obs: Determine whether we process observables in parallel when computing the VJP. :type batch_obs: bool :param wire_map: a map from wire labels to simulation indices :type wire_map: Optional[dict].

compute_derivatives(circuits: Union[QuantumTape, Sequence[QuantumTape]], 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=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None)))¶

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

compute_jvp(circuits: Union[QuantumScript, Sequence[QuantumScript]], tangents: tuple[numbers.Number, ...], 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=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None)))¶

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: Union[QuantumTape, Sequence[QuantumTape]], cotangents: Tuple[Number], 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=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None))) → Tuple¶

The vector jacobian product used in reverse-mode differentiation. Lightning[Device] uses the adjoint differentiation method to compute the VJP. :param circuits: the circuit or batch of circuits :type circuits: Union[QuantumTape, Sequence[QuantumTape]] :param cotangents: 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.

Parameters: 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: .. math:

\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. .. math:

\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(). For computing the full Jacobian, the cotangents can be batched to vectorize the computation. In this case, the cotangents can have the following shapes. batch_size below refers to the number of entries in the Jacobian: * For a state measurement, the cotangents must have shape (batch_size, 2 ** n_wires) * For n expectation values, the cotangents must have shape (n, batch_size). If n = 1,

then the shape must be (batch_size,).

eval_jaxpr(jaxpr: jax.core.Jaxpr, consts: list[pennylane.typing.TensorLike], *args) → list[pennylane.typing.TensorLike]¶

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

Parameters

jaxpr (jax.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’.

Returns

the result of evaluating the jaxpr with the given parameters.

Return type

list[TensorLike]

execute(circuits: Union[QuantumTape, Sequence[QuantumTape]], 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=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None))) → Union[Result, Sequence[Result]][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]]

execute_and_compute_derivatives(circuits: Union[QuantumTape, Sequence[QuantumTape]], 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=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None))) → Tuple¶

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

execute_and_compute_jvp(circuits: Union[QuantumScript, Sequence[QuantumScript]], tangents: tuple[numbers.Number, ...], 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=None, derivative_order=1, mcm_config=MCMConfig(mcm_method=None, postselect_mode=None)))¶

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

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.

setup_execution_config(config: Optional[ExecutionConfig] = 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.
Returns: The updated ExecutionConfig object
Return type: ExecutionConfig

simulate(circuit: QuantumScript, state: LightningGPUStateVector, postselect_mode: Optional[str] = None) → Result[source]¶

Simulate a single quantum script.

Parameters

circuit (QuantumTape) – The single circuit to simulate
state (LightningGPUStateVector) – handle to Lightning state vector
postselect_mode (str) – Configuration for handling shots with mid-circuit measurement postselection. Use "hw-like" to discard invalid shots and "fill-shots" to keep the same number of shots. Default is None.

Returns

The results of the simulation

Return type

Tuple[TensorLike]

Note that this function can return measurements for non-commuting observables simultaneously.

simulate_and_jacobian(circuit: QuantumTape, state, batch_obs: bool = False, wire_map: Optional[dict] = None) → Tuple¶

Simulate a single quantum script and compute its Jacobian.

Parameters

circuit (QuantumTape) – The single circuit to simulate
state (Lightning [Device] StateVector) – handle to the Lightning state vector
batch_obs (bool) – Determine whether we process observables in parallel when computing the jacobian. Default is False.
wire_map (Optional[dict]) – a map from wire labels to simulation indices

Returns

The results of the simulation and the calculated Jacobian

Return type

Tuple[TensorLike]

Note that this function can return measurements for non-commuting observables simultaneously.

simulate_and_vjp(circuit: QuantumTape, cotangents: Tuple[Number], state, batch_obs: bool = False, wire_map: Optional[dict] = None) → Tuple¶

Simulate a single quantum script and compute its Vector-Jacobian Product (VJP). :param circuit: The single circuit to simulate :type circuit: QuantumTape :param cotangents: 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.

Parameters

state (Lightning [Device] StateVector) – handle to the Lightning state vector
batch_obs (bool) – Determine whether we process observables in parallel when computing the jacobian.
wire_map (Optional[dict]) – a map from wire labels to simulation indices

Returns

The results of the simulation and the calculated VJP

Return type

Tuple[TensorLike]

Note that this function can return measurements for non-commuting observables simultaneously.

supports_derivatives(execution_config: Optional[ExecutionConfig] = None, circuit: Optional[QuantumTape] = None) → bool[source]¶

Check whether or not derivatives are available for a given configuration and circuit.

LightningGPU supports adjoint differentiation with analytic results.

Parameters

execution_config (ExecutionConfig) – The configuration of the desired derivative calculation
circuit (QuantumTape) – An optional circuit to check derivatives support for.

Returns

Whether or not a derivative can be calculated provided the given information

Return type

Bool

supports_jvp(execution_config: Optional[ExecutionConfig] = None, circuit: Optional[QuantumScript] = 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: Optional[ExecutionConfig] = None, circuit: Optional[QuantumTape] = None) → bool¶

Whether or not this device defines a custom vector jacobian product. Lightning[Device] will check if supports adjoint differentiation with analytic results. :param execution_config: The configuration of the desired derivative calculation :type execution_config: ExecutionConfig :param circuit: An optional circuit to check derivatives support for. :type circuit: QuantumTape

Returns: Whether or not a derivative can be calculated provided the given information
Return type: Bool

vjp(circuit: QuantumTape, cotangents: Tuple[Number], state, batch_obs: bool = False, wire_map: Optional[dict] = None)¶

Compute the Vector-Jacobian Product (VJP) for a single quantum script. :param circuit: The single circuit to simulate :type circuit: QuantumTape :param cotangents: 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.

Parameters

state (Lightning [Device] StateVector) – handle to the Lightning state vector
batch_obs (bool) – Determine whether we process observables in parallel when computing the VJP.
wire_map (Optional[dict]) – a map from wire labels to simulation indices

Returns

The VJP of the quantum script

Return type

TensorLike

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LightningGPU¶

Attributes

Methods

Post processing function and derivatives

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LightningGPU¶

Attributes

Methods

Post processing function and derivatives

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