Operator must be unitary

PennyLane Help
1

I was going to run your example about quantum kernel, but i encountered this error: "ValueError: Operator must be unitary".

@qml.qnode(dev_kernel)
def kernel(x1, x2):
    """The quantum kernel."""
    AngleEmbedding(x1, wires=range(n_qubits))
    qml.inv(AngleEmbedding(x2, wires=range(n_qubits)))
    return qml.expval(qml.Hermitian(projector, wires=range(n_qubits)))

print(kernel(X_train[0], X_train[0]))
2

Hi @sassan_moradi!

What is your projector variable in this case?

3

Here is projector variable:
projector = np.zeros((pow(2,n_qubits), pow(2,n_qubits)))
projector[0, 0] = 1

5

Hi @sassan_moradi,

Perhaps you could share a complete (non-)working example of your code? I ran your code above (with some guesses about missing lines) and was not able to reproduce the error (it gave an output without error).

6

The code is one of pennylane examples:

import numpy as np

from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

import pennylane as qml
from pennylane.templates import AngleEmbedding


X, y = load_iris(return_X_y=True) 


X = X[:100]
y = y[:100]


scaler = StandardScaler().fit(X)
X_scaled = scaler.transform(X)


y_scaled = 2 * (y - 0.5)

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled)


######################################################################
# We use the `angle-embedding
# template <https://pennylane.readthedocs.io/en/stable/code/api/pennylane.templates.embeddings.AngleEmbedding.html>`__
# which needs as many qubits as there are features:
#

n_qubits = len(X_train[0])

dev_kernel = qml.device("default.qubit", wires=n_qubits)

projector = np.zeros((2**n_qubits, 2**n_qubits))
projector[0, 0] = 1

@qml.qnode(dev_kernel)
def kernel(x1, x2):
    """The quantum kernel."""
    AngleEmbedding(x1, wires=range(n_qubits))
    qml.inv(AngleEmbedding(x2, wires=range(n_qubits)))
    return qml.expval(qml.Hermitian(projector, wires=range(n_qubits)))

kernel(X_train[0], X_train[0])

def kernel_matrix(A, B):
    """Compute the matrix whose entries are the kernel
       evaluated on pairwise data from sets A and B."""
    return np.array([[kernel(a, b) for b in B] for a in A])

svm = SVC(kernel=kernel_matrix).fit(X_train, y_train)


######################################################################
# Let’s compute the accuracy on the test set.
#

predictions = svm.predict(X_test)
7

problem solved. After upgrading pennylane, i could run it without error.

8

Glad you got it working @sassan_moradi :slight_smile: