qml.workflow.jacobian_products.DeviceJacobianProducts

class DeviceJacobianProducts(device, execution_config)

Bases: pennylane.workflow.jacobian_products.JacobianProductCalculator

Compute jacobian products using the native device methods.

Parameters

• device (pennylane.devices.Device) – the device for execution and derivatives. Must define both the vjp and jvp.

• execution_config (pennylane.devices.ExecutionConfig) – a datastructure containing the options needed to fully describe the execution.

>>> dev = qml.device('default.qubit')
>>> config = qml.devices.ExecutionConfig(gradient_method="adjoint")
>>> jpc = DeviceJacobianProducts(dev, config)

This class relies on compute_vjp() and execute_and_compute_jvp(), and works strictly for the newer device interface Device. This contrasts DeviceDerivatives which works for both device interfaces and requests the full jacobian from the device.

Methods

compute_jacobian(tapes)	Compute the full Jacobian for a batch of tapes.
compute_vjp(tapes, dy)	Compute the vjp for a given batch of tapes.
execute_and_compute_jacobian(tapes)	Compute the results and the full Jacobian for a batch of tapes.
execute_and_compute_jvp(tapes, tangents)	Calculate both the results for a batch of tapes and the jvp.

compute_jacobian(tapes)

Compute the full Jacobian for a batch of tapes.

This method is required to compute Jacobians in the tensorflow interface

Parameters

tapes (tuple[QuantumScript]) – the batch of tapes to take the derivatives of

Examples:

For an instance of JacobianProductCalculator jpc, we have:

>>> tape0 = qml.tape.QuantumScript([qml.RX(0.1, wires=0)], [qml.expval(qml.Z(0))])
>>> tape1 = qml.tape.QuantumScript([qml.RY(0.2, wires=0)], [qml.expval(qml.Z(0)), qml.expval(qml.X(0))])
>>> batch = (tape0, tape1)
>>> jpc.compute_jacobian(batch)
(array(-0.09983342), (array(-0.19866933), array(0.98006658)))

While this method could support non-scalar parameters in theory, no implementation currently supports jacobians with non-scalar parameters.

compute_vjp(tapes, dy)

Compute the vjp for a given batch of tapes.

This method is used by autograd, torch, and tensorflow to compute VJPs.

Parameters

• tapes (tuple[QuantumScript]) – the batch of tapes to take the derivatives of

• dy (tuple[tuple[TensorLike]]) – the derivatives of the results of an execution. The i``th entry (cotangent) corresponds to the ``i th tape, and the j th entry of the i th cotangent corresponds to the j th return value of the i th tape.

Returns

the vector jacobian product.

Return type

TensorLike

Examples:

For an instance of JacobianProductCalculator jpc, we have:

>>> tape0 = qml.tape.QuantumScript([qml.RX(0.1, wires=0)], [qml.expval(qml.Z(0))])
>>> tape1 = qml.tape.QuantumScript([qml.RY(0.2, wires=0)], [qml.expval(qml.Z(0)), qml.expval(qml.X(0))])
>>> batch = (tape0, tape1)
>>> dy0 = (0.5, )
>>> dy1 = (2.0, 3.0)
>>> dys = (dy0, dy1)
>>> vjps = jpc.compute_vjp(batch, dys)
>>> vjps
(array([-0.04991671]), array([2.54286107]))
>>> expected_vjp0 = 0.5 * -np.sin(0.1)
>>> qml.math.allclose(vjps[0], expected_vjp0)
True
>>> expected_vjp1 = 2.0 * -np.sin(0.2) + 3.0 * np.cos(0.2)
>>> qml.math.allclose(vjps[1], expected_vjp1)
True

While this method could support non-scalar parameters in theory, no implementation currently supports jacobians with non-scalar parameters.

execute_and_compute_jacobian(tapes)

Compute the results and the full Jacobian for a batch of tapes.

This method is required to compute Jacobians in the jax-jit interface

Parameters

tapes (tuple[QuantumScript]) – the batch of tapes to take the derivatives of

Examples:

For an instance of JacobianProductCalculator jpc, we have:

>>> tape0 = qml.tape.QuantumScript([qml.RX(0.1, wires=0)], [qml.expval(qml.Z(0))])
>>> tape1 = qml.tape.QuantumScript([qml.RY(0.2, wires=0)], [qml.expval(qml.Z(0)), qml.expval(qml.X(0))])
>>> batch = (tape0, tape1)
>>> results, jacs = jpc.execute_and_compute_jacobian(batch)
>>> results
(0.9950041652780258, (0.9800665778412417, 0.19866933079506116))
>>> jacs
(array(-0.09983342), (array(-0.19866933), array(0.98006658)))

While this method could support non-scalar parameters in theory, no implementation currently supports jacobians with non-scalar parameters.

execute_and_compute_jvp(tapes, tangents)

Calculate both the results for a batch of tapes and the jvp.

This method is required to compute JVPs in the JAX interface.

Parameters

• tapes (Sequence[QuantumScript | QuantumTape]) – The batch of tapes to take the derivatives of

• tangents (Sequence[Sequence[TensorLike]]) – the tangents for the parameters of the tape. The i th tangent corresponds to the i th tape, and the j th entry into a tangent entry corresponds to the j th trainable parameter of the tape.

Returns

the results of the execution and the jacobian vector product

Return type

ResultBatch, TensorLike

Examples:

For an instance of JacobianProductCalculator jpc, we have:

>>> tape0 = qml.tape.QuantumScript([qml.RX(0.1, wires=0)], [qml.expval(qml.Z(0))])
>>> tape1 = qml.tape.QuantumScript([qml.RY(0.2, wires=0)], [qml.expval(qml.Z(0))])
>>> batch = (tape0, tape1)
>>> tangents0 = (1.5, )
>>> tangents1 = (2.0, )
>>> tangents = (tangents0, tangents1)
>>> results, jvps = jpc.execute_and_compute_jvp(batch, tangents)
>>> expected_results = (np.cos(0.1), np.cos(0.2))
>>> qml.math.allclose(results, expected_results)
True
>>> jvps
(array(-0.14975012), array(-0.39733866))
>>> expected_jvps = 1.5 * -np.sin(0.1), 2.0 * -np.sin(0.2)
>>> qml.math.allclose(jvps, expected_jvps)
True

While this method could support non-scalar parameters in theory, no implementation currently supports jacobians with non-scalar parameters.

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