Gradients and training

PennyLane offers seamless integration between classical and quantum computations. Code up quantum circuits in PennyLane, compute gradients of quantum circuits, and connect them easily to the top scientific computing and machine learning libraries.

Training and interfaces

The bridge between the quantum and classical worlds is provided in PennyLane via interfaces to automatic differentiation libraries. Currently, four libraries are supported: NumPy, PyTorch, JAX, and TensorFlow. PennyLane makes each of these libraries quantum-aware, allowing quantum circuits to be treated just like any other operation. Any automatic differentiation framework can be chosen with any device.

In PennyLane, an automatic differentiation framework is declared using the interface argument when creating a QNode, e.g.,

@qml.qnode(dev, interface="tf")
def my_quantum_circuit(...):
    ...

Note

If no interface is specified, PennyLane will default to the NumPy interface (powered by the autograd library).

This will allow native numerical objects of the specified library (NumPy arrays, JAX arrays, Torch Tensors, or TensorFlow Tensors) to be passed as parameters to the quantum circuit. It also makes the gradients of the quantum circuit accessible to the classical library, enabling the optimization of arbitrary hybrid circuits by making use of the library’s native optimizers.

When specifying an interface, the objects of the chosen framework are converted into NumPy objects and are passed to a device in most cases. Exceptions include cases when the devices support end-to-end computations in a framework. Such devices may be referred to as backpropagation or passthru devices.

See the links below for walkthroughs of each specific interface:

In addition to the core automatic differentiation frameworks discussed above, PennyLane also provides higher-level classes for converting QNodes into both Keras and torch.nn layers:

pennylane.qnn.KerasLayer(qnode, …)

Converts a QNode() to a Keras Layer.

pennylane.qnn.TorchLayer(qnode, …)

Converts a QNode() to a Torch layer.

Note

QNodes that allow for automatic differentiation will always incur a small overhead on evaluation. If you do not need to compute quantum gradients of a QNode, specifying interface=None will remove this overhead and result in a slightly faster evaluation. However, gradients will no longer be available.

Optimizers

Optimizers are objects which can be used to automatically update the parameters of a quantum or hybrid machine learning model. The optimizers you should use are dependent on your choice of the classical autodifferentiation library, and are available from different access points.

NumPy

When using the standard NumPy framework, PennyLane offers some built-in optimizers. Some of these are specific to quantum optimization, such as the QNGOptimizer, LieAlgebraOptimizer RotosolveOptimizer, RotoselectOptimizer, ShotAdaptiveOptimizer, and QNSPSAOptimizer.

AdagradOptimizer

Gradient-descent optimizer with past-gradient-dependent learning rate in each dimension.

AdamOptimizer

Gradient-descent optimizer with adaptive learning rate, first and second moment.

GradientDescentOptimizer

Basic gradient-descent optimizer.

LieAlgebraOptimizer

Lie algebra optimizer.

MomentumOptimizer

Gradient-descent optimizer with momentum.

NesterovMomentumOptimizer

Gradient-descent optimizer with Nesterov momentum.

QNGOptimizer

Optimizer with adaptive learning rate, via calculation of the diagonal or block-diagonal approximation to the Fubini-Study metric tensor.

RMSPropOptimizer

Root mean squared propagation optimizer.

RotosolveOptimizer

Rotosolve gradient-free optimizer.

RotoselectOptimizer

Rotoselect gradient-free optimizer.

ShotAdaptiveOptimizer

Optimizer where the shot rate is adaptively calculated using the variances of the parameter-shift gradient.

SPSAOptimizer

The Simultaneous Perturbation Stochastic Approximation method (SPSA) is a stochastic approximation algorithm for optimizing cost functions whose evaluation may involve noise.

QNSPSAOptimizer

Quantum natural SPSA (QNSPSA) optimizer.

PyTorch

If you are using the PennyLane PyTorch framework, you should import one of the native PyTorch optimizers (found in torch.optim).

TensorFlow

When using the PennyLane TensorFlow framework, you will need to leverage one of the TensorFlow optimizers (found in tf.keras.optimizers).

JAX

Check out the JAXopt and the Optax packages to find optimizers for the PennyLane JAX framework.

Gradients

The interface between PennyLane and automatic differentiation libraries relies on PennyLane’s ability to compute or estimate gradients of quantum circuits. There are different strategies to do so, and they may depend on the device used.

When creating a QNode, you can specify the differentiation method like this:

@qml.qnode(dev, diff_method="parameter-shift")
def circuit(x):
    qml.RX(x, wires=0)
    return qml.probs(wires=0)

PennyLane currently provides the following differentiation methods for QNodes:

Simulation-based differentiation

The following methods use reverse accumulation to compute gradients; a well-known example of this approach is backpropagation. These methods are not hardware compatible; they are only supported on statevector simulator devices such as default.qubit.

However, for rapid prototyping on simulators, these methods typically out-perform forward-mode accumulators such as the parameter-shift rule and finite-differences. For more details, see the quantum backpropagation demonstration.

  • "backprop": Use standard backpropagation.

    This differentiation method is only allowed on simulator devices that are classically end-to-end differentiable, for example default.qubit. This method does not work on devices that estimate measurement statistics using a finite number of shots; please use the parameter-shift rule instead.

  • "adjoint": Use a form of backpropagation that takes advantage of the unitary or reversible nature of quantum computation.

    The adjoint method reverses through the circuit after a forward pass by iteratively applying the inverse (adjoint) gate. This method is similar to "backprop", but has significantly lower memory usage and a similar runtime.

Hardware-compatible differentiation

The following methods support both quantum hardware and simulators, and are examples of forward accumulation. However, when using a simulator, you may notice that the time required to compute the gradients with these methods scales linearly with the number of trainable circuit parameters.

  • "parameter-shift": Use the analytic parameter-shift rule for all supported quantum operation arguments, with finite-difference as a fallback.

  • "finite-diff": Use numerical finite-differences for all quantum operation arguments.

Device gradients

  • "device": Queries the device directly for the gradient. Only allowed on devices that provide their own gradient computation.

Note

If not specified, the default differentiation method is diff_method="best". PennyLane will attempt to determine the best differentiation method given the device and interface. Typically, PennyLane will prioritize device-provided gradients, backpropagation, parameter-shift rule, and finally finite differences, in that order.

Gradient transforms

In addition to registering the differentiation method of QNodes to be used with autodifferentiation frameworks, PennyLane also provides a library of gradient transforms via the qml.gradients module.

Quantum gradient transforms are strategies for computing the gradient of a quantum circuit that work by transforming the quantum circuit into one or more gradient circuits. They accompany these circuits with a function that post-processes their output. These gradient circuits, once executed and post-processed, return the gradient of the original circuit.

Examples of quantum gradient transforms include finite-difference rules and parameter-shift rules; these can be applied directly to QNodes:

dev = qml.device("default.qubit", wires=2)

@qml.qnode(dev)
def circuit(weights):
    qml.RX(weights[0], wires=0)
    qml.RY(weights[1], wires=1)
    qml.CNOT(wires=[0, 1])
    qml.RX(weights[2], wires=1)
    return qml.probs(wires=1)
>>> weights = np.array([0.1, 0.2, 0.3], requires_grad=True)
>>> circuit(weights)
tensor([0.9658079, 0.0341921], requires_grad=True)
>>> qml.gradients.param_shift(circuit)(weights)
tensor([[-0.04673668, -0.09442394, -0.14409127],
        [ 0.04673668,  0.09442394,  0.14409127]], requires_grad=True)

Note that, while gradient transforms allow quantum gradient rules to be applied directly to QNodes, this is not a replacement — and should not be used instead of — standard training workflows (for example, qml.grad() if using Autograd, loss.backward() for PyTorch, or tape.gradient() for TensorFlow). This is because gradient transforms do not take into account classical computation nodes, and only support gradients of QNodes. For more details on available gradient transforms, as well as learning how to define your own gradient transform, please see the qml.gradients documentation.

Supported configurations

The table below show all the currently supported functionality for the "default.qubit" device. At the moment, it takes into account the following parameters:

  • The interface, e.g. "jax"

  • The differentiation method, e.g. "parameter-shift"

  • The return value of the QNode, e.g. qml.expval() or qml.probs()

  • The number of shots, either None or an integer > 0

Return type

Interface

Differentiation method

state

density matrix

probs

sample

expval (obs)

expval (herm)

expval (proj)

var

vn entropy

mutual info

None

"device"

1

1

1

1

1

1

1

1

1

1

"backprop"

1

1

1

1

1

1

1

1

1

1

"adjoint"

2

2

2

2

2

2

2

2

2

2

"parameter-shift"

2

2

2

2

2

2

2

2

2

2

"finite-diff"

2

2

2

2

2

2

2

2

2

2

"autograd"

"device"

3

3

3

3

3

3

3

3

3

3

"backprop"

4

4

5

9

5

5

5

5

5

5

"adjoint"

6

6

6

6

7

7

7

6

6

6

"parameter-shift"

10

10

8

9

8

8

8

8

10

10

"finite-diff"

10

10

8

9

8

8

8

8

8

8

"jax"

"device"

3

3

3

3

3

3

3

3

3

3

"backprop"

5

5

5

9

5

5

5

5

5

5

"adjoint"

6

6

6

6

7

7

7

6

6

6

"parameter-shift"

10

10

8

9

8

8

8

8

10

10

"finite-diff"

10

10

8

9

8

8

8

8

8

8

"tf"

"device"

3

3

3

3

3

3

3

3

3

3

"backprop"

5

5

5

9

5

5

5

5

5

5

"adjoint"

6

6

6

6

7

7

7

6

6

6

"parameter-shift"

10

10

8

9

8

8

8

8

10

10

"finite-diff"

10

10

8

9

8

8

8

8

8

8

"torch"

"device"

3

3

3

3

3

3

3

3

3

3

"backprop"

5

5

5

9

5

5

5

5

5

5

"adjoint"

6

6

6

6

7

7

7

6

6

6

"parameter-shift"

10

10

8

9

8

8

8

8

10

10

"finite-diff"

10

10

8

9

8

8

8

8

8

8

  1. Not supported. Gradients are not computed even though diff_method is provided. Fails with error.

  2. Not supported. Gradients are not computed even though diff_method is provided. Warns that no auto-differentiation framework is being used, but does not fail. Forward pass is still supported.

  3. Not supported. The default.qubit device does not provide a native way to compute gradients. See Device jacobian for details.

  4. Supported, but only when shots=None. See Backpropagation for details.

    If the circuit returns a state, then the circuit itself is not differentiable directly. However, any real scalar-valued post-processing done to the output of the circuit will be differentiable. See State gradients for details.

  5. Supported, but only when shots=None. See Backpropagation for details.

  6. Not supported. The adjoint differentiation algorithm is only implemented for computing the expectation values of observables. See Adjoint differentation for details.

  7. Supported. Raises warning when shots>0 since the gradient is always computed analytically. See Adjoint differentation for details.

  8. Supported.

  9. Not supported. The discretization of the output caused by wave function collapse is not differentiable. The forward pass is still supported. See Sample gradients for details.

  10. Not supported. “We just don’t have the theory yet.”