# NumPy interface¶

Note

This interface is the default interface supported by PennyLane’s QNode.

## Using the NumPy interface¶

Using the NumPy interface is easy in PennyLane, and the default approach — designed so it will feel like you are just using standard NumPy, with the added benefit of automatic differentiation.

All you have to do is make sure to import the wrapped version of NumPy provided by PennyLane:

>>> from pennylane import numpy as np


This is provided via autograd, and enables automatic differentiation and backpropagation of classical computations using familiar NumPy functions and modules (such as np.sin, np.cos, np.exp, np.linalg, np.fft), as well as standard Python constructs, such as if statements, and for and while loops.

### Via the QNode decorator¶

The QNode decorator is the recommended way for creating QNodes in PennyLane. By default, all QNodes are constructed for the NumPy interface, but this can also be specified explicitly by passing the interface='numpy' keyword argument:

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

@qml.qnode(dev, interface='numpy')
def circuit(phi, theta):
qml.RX(phi, wires=0)
qml.RY(phi, wires=1)
qml.CNOT(wires=[0, 1])
qml.PhaseShift(theta, wires=0)


The QNode circuit() is a NumPy-interfacing QNode, accepting standard Python data types such as ints, floats, lists, and tuples, as well as NumPy arrays, and returning NumPy arrays.

It can now be used like any other Python/NumPy function:

>>> phi = np.array([0.5, 0.1])
>>> theta = 0.2
>>> circuit(phi, theta)
array([ 0.87758256,  0.68803733])


### Via the QNode class¶

Sometimes, it is more convenient to instantiate a QNode object directly, for example, if you would like to reuse the same quantum function across multiple devices, or even using different classical interfaces:

dev1 = qml.device('default.qubit', wires=2)
dev2 = qml.device('forest.wavefunction', wires=2)

def circuit(phi, theta):
qml.RX(phi, wires=0)
qml.RY(phi, wires=1)
qml.CNOT(wires=[0, 1])
qml.PhaseShift(theta, wires=0)

qnode1 = qml.QNode(circuit, dev1)
qnode2 = qml.QNode(circuit, dev2)


By default, all QNodes created this way are NumPy interfacing QNodes.

To calculate the gradient of a NumPy-QNode, we can simply use the provided grad() function.

For example, consider the following QNode:

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

@qml.qnode(dev)
def circuit(phi, theta):
qml.RX(phi, wires=0)
qml.RY(phi, wires=1)
qml.CNOT(wires=[0, 1])
qml.PhaseShift(theta, wires=0)
return qml.expval(qml.PauliZ(0))


We can now use grad() to create a QNode gradient function, with respect to both QNode parameters phi and theta:

>>> phi = np.array([0.5, 0.1])
>>> theta = 0.2
>>> dcircuit = qml.grad(circuit, argnum=[0, 1])


Evaluating this gradient function at specific parameter values:

>>> dcircuit(phi, theta)
(array([ -4.79425539e-01,   1.11022302e-16]), array(0.0))


## Optimization¶

To optimize your hybrid classical-quantum model using the NumPy interface, use the provided PennyLane optimizers.

For example, we can optimize a NumPy-interfacing QNode (below) such that the weights x lead to a final expectation value of 0.5:

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

@qml.qnode(dev)
def circuit(x):
qml.RX(x, wires=0)
qml.RZ(x, wires=1)
qml.CNOT(wires=[0, 1])
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))

def cost(x):
return np.abs(circuit(x) - 0.5)**2

steps = 100
params = np.array([0.011, 0.012, 0.05])

for i in range(steps):
# update the circuit parameters
params = opt.step(cost, params)


The final weights and circuit value are:

>>> params
array([ 0.19846757,  0.012     ,  1.03559806])
>>> circuit(params)
0.5


For more details on the NumPy optimizers, check out the tutorials, as well as the Optimization methods documentation.

## Vector-valued QNodes and the Jacobian¶

How does automatic differentiation work in the case where the QNode returns multiple expectation values? If we were to naively try computing the gradient using the grad() function,

g1 = qml.grad(circuit1, argnum=0)
g1(np.pi/2)


we would get an error message. This is because the gradient is only defined for scalar functions, i.e., functions which return a single value. In the case where the QNode returns multiple expectation values, the correct differential operator to use is the Jacobian matrix. This can be accessed in PennyLane as jacobian():

>>> j1 = qml.jacobian(circuit1, argnum=0)
>>> j1(np.pi/2)
array([-1., -1.])


The output of jacobian() is a two-dimensional vector, with the first/second element being the partial derivative of the first/second expectation value with respect to the input parameter. The Jacobian function has the same signature as the gradient function, requiring the user to specify which argument should be differentiated.

If you want to compute the Jacobian matrix for a function with multiple input parameters and multiple expectation values, the recommended way to do this is to combine the parameters into a single list/array and index into this inside your quantum circuit function. Consider the following circuit:

@qml.qnode(dev)
def circuit2(params):
qml.RX(params, wires=0)
qml.RZ(params, wires=0)
qml.RX(params, wires=1)
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))


It has a full Jacobian with two rows and three columns:

>>> j2 = qml.jacobian(circuit2, argnum=0)
>>> j2(np.pi / 3, 0.25, np.pi / 2)
>>> array([[-8.66025404e-01, -5.55111512e-17,  0.00000000e+00],
[-4.71844785e-16, -1.38777878e-17, -5.00000000e-01]])


Warning

Currently, pennylane.jacobian() supports only the case where argnum is a single integer. For quantum functions with multiple arguments, use the above method to get the full Jacobian matrix.

The PennyLane NumPy interface leverages the Python library autograd to enable automatic differentiation of NumPy code, and extends it to provide gradients of quantum circuit functions encapsulated in QNodes. In order to make NumPy code differentiable, Autograd provides a wrapped version of NumPy (exposed in PennyLane as pennylane.numpy).

As stated in other sections, using this interface, any hybrid computation should be coded using the wrapped version of NumPy provided by PennyLane. If you accidentally import the vanilla version of NumPy, your code will not be automatically differentiable.

Because of the way autograd wraps NumPy, the PennyLane NumPy interface does not require users to learn a new mini-language for declaring classical computations, or invoke awkward language-dependent functions which replicate basic python control-flow statements (if statements, loops, etc.). Users can continue using many of the standard numerical programming practices common in Python and NumPy.

That being said, autograd’s coverage of NumPy is not complete. It is best to consult the autograd docs for a more complete overview of supported and unsupported features. We highlight a few of the major ‘gotchas’ here.

Do not use:

• Assignment to arrays, such as A[0, 0] = x.

• Implicit casting of lists to arrays, for example A = np.sum([x, y]). Make sure to explicitly cast to a NumPy array first, i.e., A = np.sum(np.array([x, y])) instead.

• A.dot(B) notation. Use np.dot(A, B) or A @ B instead.

• In-place operations such as a += b. Use a = a + b instead.

• Some isinstance checks, like isinstance(x, np.ndarray) or isinstance(x, tuple), without first doing from autograd.builtins import isinstance, tuple.