LightningTensor

class LightningTensor(*, wires=None, shots=None, method: str = 'mps', c_dtype=<class 'numpy.complex128'>, **kwargs)

Bases: Device

PennyLane Lightning Tensor device.

A device to perform tensor network operations on a quantum circuit.

This device is designed to simulate large-scale quantum circuits using tensor network methods. For small circuits, other devices like lightning.qubit, lightning.gpu``or ``lightning.kokkos are recommended.

Currently, only the Matrix Product State (MPS) method as implemented in the cutensornet backend is supported.

Parameters

• wires (int) – The number of wires to initialize the device with. Defaults to None if not specified.

• shots (int) – Measurements are performed drawing shots times from a discrete random variable distribution associated with a state vector and an observable. Defaults to None if not specified. Setting to None results in computing statistics like expectation values and variances analytically.

• method (str) – Supported method. Currently, only mps is supported.

• c_dtype – Datatypes for the tensor representation. Must be one of numpy.complex64 or numpy.complex128. Default is numpy.complex128.

Keyword Arguments

• max_bond_dim (int) – The maximum bond dimension to be used in the MPS simulation. Default is 128. The accuracy of the wavefunction representation comes with a memory tradeoff which can be tuned with max_bond_dim. The larger the internal bond dimension, the more entanglement can be described but the larger the memory requirements. Note that GPUs are ill-suited (i.e. less competitive compared with CPUs) for simulating circuits with low bond dimensions and/or circuit layers with a single or few gates because the arithmetic intensity is lower.

• cutoff (float) – The threshold used to truncate the singular values of the MPS tensors. The default is 0.

• cutoff_mode (str) – Singular value truncation mode. The options are "rel" and "abs". The default is "abs".

• backend (str) – Supported backend. Currently, only cutensornet is supported.

Example

import pennylane as qml

num_qubits = 100

dev = qml.device("lightning.tensor", wires=num_qubits)

@qml.qnode(dev)
def circuit(num_qubits):
    for qubit in range(0, num_qubits - 1):
        qml.CZ(wires=[qubit, qubit + 1])
        qml.X(wires=[qubit])
        qml.Z(wires=[qubit + 1])
    return qml.expval(qml.Z(0))

>>> print(circuit(num_qubits))
-1.0

Attributes

backend	Supported backend.
c_dtype	Tensor 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	Tensor complex data type.
method	Supported method.
name	The name of the device.
num_wires	Number of wires addressed on this device.
observables
operations
shots	Default shots for execution workflows containing this device.
tracker	A Tracker that can store information about device executions, shots, batches, intermediate results, or any additional device dependent information.
wires	The device wires.

backend

Supported backend.

c_dtype

Tensor complex data type.

capabilities: Optional[DeviceCapabilities] = None

A DeviceCapabilities object describing the capabilities of the backend device.

config_filepath: Optional[str] = None

A device can use a toml file to specify the capabilities of the backend device. If this is provided, the file will be loaded into a DeviceCapabilities object assigned to the capabilities attribute.

dtype

Tensor complex data type.

method

Supported method.

name

The name of the device.

num_wires

Number of wires addressed on this device.

observables = frozenset({'Exp', 'Hadamard', 'Hermitian', 'Identity', 'LinearCombination', 'PauliX', 'PauliY', 'PauliZ', 'Prod', 'SProd', 'Sum'})

operations = frozenset({'Adjoint(ISWAP)', 'Adjoint(S)', 'Adjoint(SISWAP)', 'Adjoint(SX)', 'Adjoint(T)', 'BasisState', 'BlockEncode', 'C(BlockEncode)', 'C(DoubleExcitation)', 'C(DoubleExcitationMinus)', 'C(DoubleExcitationPlus)', 'C(GlobalPhase)', 'C(Hadamard)', 'C(IsingXX)', 'C(IsingXY)', 'C(IsingYY)', 'C(IsingZZ)', 'C(MultiRZ)', 'C(PhaseShift)', 'C(RX)', 'C(RY)', 'C(RZ)', 'C(Rot)', 'C(S)', 'C(SingleExcitation)', 'C(SingleExcitationMinus)', 'C(SingleExcitationPlus)', 'C(T)', 'CNOT', 'CRX', 'CRY', 'CRZ', 'CRot', 'CSWAP', 'CY', 'CZ', 'ControlledPhaseShift', 'ControlledQubitUnitary', 'DiagonalQubitUnitary', 'DoubleExcitation', 'DoubleExcitationMinus', 'DoubleExcitationPlus', 'ECR', 'GlobalPhase', 'Hadamard', 'ISWAP', 'Identity', 'IsingXX', 'IsingXY', 'IsingYY', 'IsingZZ', 'OrbitalRotation', 'PSWAP', 'PauliX', 'PauliY', 'PauliZ', 'PhaseShift', 'QubitCarry', 'QubitSum', 'QubitUnitary', 'RX', 'RY', 'RZ', 'Rot', 'S', 'SISWAP', 'SQISW', 'SWAP', 'SX', 'SingleExcitation', 'SingleExcitationMinus', 'SingleExcitationPlus', 'T', 'Toffoli'})

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.
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.
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.
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.

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 data structure 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)))

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 data structure with all additional information required for execution.

Returns

A numeric result of computing the vector-Jacobian product.

Return type

tensor-like

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]]

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 data structure 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)))

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 data structure 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

See also

execute() and compute_jvp()

execute_and_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)))

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.

• execution_config (ExecutionConfig) – a data structure 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

preprocess(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)))

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

Parameters

execution_config (Union[ExecutionConfig, Sequence[ExecutionConfig]]) – A data structure describing the parameters needed to fully describe the execution.

Returns

A transform program that when called returns QuantumTape’s that the device can natively execute as well as a postprocessing function to be called after execution, and a configuration with unset specifications filled in.

Return type

TransformProgram, ExecutionConfig

This device currently:

• Does not support derivatives.

• Does not support vector-Jacobian products.

preprocess_transforms(execution_config: Optional[ExecutionConfig] = None) → TransformProgram

Returns the transform program to preprocess a circuit for execution.

Parameters

execution_config (ExecutionConfig) – The execution configuration object

Returns

A transform program that is called before execution

Return type

TransformProgram

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

• Decomposition of operations and measurements to what is supported by the device.

• Splitting a circuit with measurements of non-commuting observables or Hamiltonians into multiple executions.

• Splitting a circuit with batched parameters into multiple executions.

• Validation of wires, measurements, and observables.

• Gradient specific preprocessing, such as making sure trainable operators have generators.

Example

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

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

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

    def blank_processing_fn(results):
        return results[0]

    return [tape], processing_fn

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

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

See also

transform() and TransformProgram

Post processing function and derivatives

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

from pennylane.interfaces.jax import execute as jax_boundary

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

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

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

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

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

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

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

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

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.

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

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