LightningTensor¶

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

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, the Matrix Product State (MPS) and the Exact Tensor Network methods are supported as implemented in the cutensornet backend.

Parameters

wires (Optional[int, list]) – The number of wires to initialize the device with. Defaults to None if not specified, and the device will allocate the number of wires depending on the circuit to execute. 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. The supported methods are "mps" (Matrix Product State) and "tn" (Exact Tensor Network). Default is "mps".
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) – (Only for method=mps) 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) – (Only for method=mps) The threshold used to truncate the singular values of the MPS tensors. The default is 0.
cutoff_mode (str) – (Only for method=mps) Singular value truncation mode for MPS tensors. The options are "rel" and "abs". Default is "abs".
backend (str) – Supported backend. Currently, only cutensornet is supported. Default is cutensornet.
worksize_pref (str) – Preference for workspace size for cutensornet backend. The options are recommended and max. Default is recommended.

Example for the MPS method

import pennylane as qml

num_qubits = 100

dev = qml.device("lightning.tensor", wires=num_qubits, max_bond_dim=32)

@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

Example for the Exact Tensor Network method

import pennylane as qml

num_qubits = 100

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

@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: DeviceCapabilities | None = None¶: A DeviceCapabilities object describing the capabilities of the backend device.

config_filepath: str | None = 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)', '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', 'MPSPrep', '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.devices.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.
`dynamic_wires_from_circuit`(circuit)	Map circuit wires to Pennylane `default.qubit` standard wire order.
`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.
`jaxpr_jvp`(jaxpr, args, tangents[, ...])	An experimental method for computing the results and jvp for PLXPR.
`preprocess`([execution_config])	Device preprocessing function.
`preprocess_transforms`([execution_config])	This function defines the device transform program to be applied and an updated device configuration.
`setup_execution_config`([config, circuit])	Update the execution config with choices for how the device should be used and the device options.
`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: pennylane.devices.execution_config.ExecutionConfig | None = 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 data structure with all additional information required for execution.

Returns

The Jacobian for each trainable parameter.

Return type

Tuple

compute_jvp(circuits: pennylane.tape.qscript.QuantumScript | collections.abc.Sequence[pennylane.tape.qscript.QuantumScript], tangents: tuple[numbers.Number, ...], execution_config: pennylane.devices.execution_config.ExecutionConfig | None = 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: pennylane.devices.execution_config.ExecutionConfig | None = 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 data structure with all additional information required for execution.

Returns

A numeric result of computing the vector-Jacobian product.

Return type

tensor-like

dynamic_wires_from_circuit(circuit)[source]¶

Map circuit wires to Pennylane default.qubit standard wire order.

Parameters: circuit (QuantumTape) – The circuit to execute.
Returns: The updated circuit with the wires mapped to the standard wire order.
Return type: QuantumTape

eval_jaxpr(jaxpr: jax.extend.core.Jaxpr, consts: list[Union[int, float, bool, complex, bytes, list, tuple, numpy.ndarray, numpy.generic, autograd.numpy.numpy_boxes.ArrayBox, pennylane.typing.InterfaceTensor]], *args, execution_config: pennylane.devices.execution_config.ExecutionConfig | None = None, shots: Shots = Shots(total_shots=None, shot_vector=())) → list[Union[int, float, bool, complex, bytes, list, tuple, numpy.ndarray, numpy.generic, autograd.numpy.numpy_boxes.ArrayBox, pennylane.typing.InterfaceTensor]]¶

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

Parameters

jaxpr (jax.extend.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.

Keyword Arguments

execution_config (Optional[ExecutionConfig]) – a data structure with additional information required for execution
shots (Shots) – the number of shots to use for the evaluation

Returns

the result of evaluating the jaxpr with the given parameters.

Return type

list[TensorLike]

execute(circuits: Union[QuantumTape, Sequence[QuantumTape]], execution_config: pennylane.devices.execution_config.ExecutionConfig | None = 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 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: pennylane.devices.execution_config.ExecutionConfig | None = 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 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: pennylane.tape.qscript.QuantumScript | collections.abc.Sequence[pennylane.tape.qscript.QuantumScript], tangents: tuple[numbers.Number, ...], execution_config: pennylane.devices.execution_config.ExecutionConfig | None = 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 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.

preprocess_transforms(execution_config: pennylane.devices.execution_config.ExecutionConfig | None = None) → TransformProgram[source]¶

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.

setup_execution_config(config: pennylane.devices.execution_config.ExecutionConfig | None = None, circuit=None) → ExecutionConfig[source]¶: Update the execution config with choices for how the device should be used and the device options.

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

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: pennylane.devices.execution_config.ExecutionConfig | None = None, circuit: pennylane.tape.qscript.QuantumScript | None = 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: pennylane.devices.execution_config.ExecutionConfig | None = None, circuit: Optional[QuantumTape] = None) → bool[source]¶

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

code/api/pennylane_lightning.lightning_tensor.LightningTensor

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LightningTensor¶

Attributes

Methods

Post processing function and derivatives

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