How is functions like qml.RY, qml.CNOT get executed?

PennyLane Help Code Together
1

Hi,

I am currently studying the “Iris classification” part of this tutorial: Variational classifier — PennyLane documentation.

I added some print() in the PennyLane’s source code to find out how everything works:

@staticmethod
    def compute_matrix(theta):  # pylint: disable=arguments-differ
        r"""Representation of the operator as a canonical matrix in the computational basis (static method).

        The canonical matrix is the textbook matrix representation that does not consider wires.
        Implicitly, this assumes that the wires of the operator correspond to the global wire order.

        .. seealso:: :meth:`~.RY.matrix`


        Args:
            theta (tensor_like or float): rotation angle

        Returns:
            tensor_like: canonical matrix

        **Example**

        >>> qml.RY.compute_matrix(torch.tensor(0.5))
        tensor([[ 0.9689, -0.2474],
                [ 0.2474,  0.9689]])
        """
        print("Operation: RY")
        print("theta: ", theta)
        c = qml.math.cos(theta / 2)
        s = qml.math.sin(theta / 2)
        if qml.math.get_interface(theta) == "tensorflow":
            c = qml.math.cast_like(c, 1j)
            s = qml.math.cast_like(s, 1j)
        # The following avoids casting an imaginary quantity to reals when backpropagating
        c = (1 + 0j) * c
        s = (1 + 0j) * s
        print("RY matrix: ", qml.math.stack([stack_last([c, -s]), stack_last([s, c])], axis=-2))
        return qml.math.stack([stack_last([c, -s]), stack_last([s, c])], axis=-2)

and some print() in the tutorial code to study the flow of the complete encoding process:

def statepreparation(a):
    print("encoding.....")

    qml.RY(a[0], wires=0)
    qml.CNOT(wires=[0, 1])
    qml.RY(a[1], wires=1)
    qml.CNOT(wires=[0, 1])
    qml.RY(a[2], wires=1)
    qml.PauliX(wires=0)
    qml.CNOT(wires=[0, 1])
    qml.RY(a[3], wires=1)
    qml.CNOT(wires=[0, 1])
    qml.RY(a[4], wires=1)
    qml.PauliX(wires=0)
    print("encoding done!")

I expected the output would be
encoding....
some encoding output
encoding done!
But seems like it doesn’t print in the expected order:

output
output938×272 28.5 KB

How can I fix this?

2

Hi @pennyRocie,

Since I don’t have your full code I will try to guide you based on the Variational classifier demo. I have added the relevant code here with comments

import pennylane as qml
from pennylane import numpy as np

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

# Create a function to get the angles
def get_angles(x):

    beta0 = 2 * np.arcsin(np.sqrt(x[1] ** 2) / np.sqrt(x[0] ** 2 + x[1] ** 2 + 1e-12))
    beta1 = 2 * np.arcsin(np.sqrt(x[3] ** 2) / np.sqrt(x[2] ** 2 + x[3] ** 2 + 1e-12))
    beta2 = 2 * np.arcsin(
        np.sqrt(x[2] ** 2 + x[3] ** 2)
        / np.sqrt(x[0] ** 2 + x[1] ** 2 + x[2] ** 2 + x[3] ** 2)
    )

    return np.array([beta2, -beta1 / 2, beta1 / 2, -beta0 / 2, beta0 / 2])

# Define the statepreparation routine
def statepreparation(a):
    print("encoding.....")

    qml.RY(a[0], wires=0)
    qml.CNOT(wires=[0, 1])
    qml.RY(a[1], wires=1)
    qml.CNOT(wires=[0, 1])
    qml.RY(a[2], wires=1)
    qml.PauliX(wires=0)
    qml.CNOT(wires=[0, 1])
    qml.RY(a[3], wires=1)
    qml.CNOT(wires=[0, 1])
    qml.RY(a[4], wires=1)
    qml.PauliX(wires=0)
    print("encoding done!")

# Define your data
x = np.array([0.53896774, 0.79503606, 0.27826503, 0.0], requires_grad=False)
# Run the get_angles function
ang = get_angles(x)

# Define your QNode
@qml.qnode(dev)
def test(angles):

    statepreparation(angles)

    return qml.expval(qml.PauliZ(0))

# Run your QNode
# Within your QNode the statepreparation function will print
# "encoding....." and "encoding done!"
test(ang)

# After you run your circuit you can print other things
print("x               : ", x)
print("angles          : ", ang)
print("amplitude vector: ", np.real(dev.state))

As you can see, by running test(ang) you’re running your circuit, which is in turn running the statepreparation function. After you’re done running your circuit you print some additional values.

Please let me know if this makes it more clear for you!