I am new to QML. I was practicing with the Breast Cancer Dataset for practicing QML. It is a binary classification task. Originally, I want to work on a different dataset.

The issue in my code is: the weights are not updating after each optimization step. Weights should change, so that my cost can reduce.

```
# Binnary classification for Breast Cancer Dataset. Issue: Weights are not updating during optimization
# importing libraries
import pennylane as qml
from pennylane import numpy as np
from sklearn.datasets import load_breast_cancer
# loading data
data, target = load_breast_cancer(return_X_y=True) # data shape -> (569, 30)
# making target: {0, 1} ---> {-1, 1}
for i in range(len(target)):
if target[i] == 0:
target[i] = -1
# device initialization
dev = qml.device('default.qubit', shots = 100)
# declaring weights
weights = np.random.random(size=(5), requires_grad=True)
# circuit
@qml.qnode(dev)
def circuit(weights, X):
# amplitude encoding
qml.AmplitudeEmbedding(X, wires=[0,1,2,3,4], normalize=True, pad_with=0)
# variational layers
for i in range(5):
qml.RX(weights[i], wires=i)
qml.RX(weights[i], wires=i)
qml.RY(weights[i], wires=i)
for ii in ([0,1], [1,2], [2,3], [3,4], [4,0]):
qml.CNOT(wires=ii)
return qml.expval(qml.PauliZ(0))
def predictor(weights): # function to find the y_pred
y_pred = []
for row in data:
pred = np.sign(circuit(weights, row))
if pred == 0:
pred = 1
y_pred.append(pred)
return y_pred
def cost(weights): # function to find cost
y_pred = predictor(weights)
return np.mean((target-y_pred)**2)
def accuracy(target, y_pred): # function to find accuracy in classification
acc = sum(abs(l - p) < 1e-5 for l, p in zip(target, y_pred))
acc = acc / len(target)
return acc
opt = qml.GradientDescentOptimizer(0.001) # declaration of optimizer
steps = 100 # number of steps
for ii in range(steps):
weights = opt.step(cost, weights)
if ii%10 == 0:
print(f'cost at step {ii+1} is: {cost(weights)}')
print(f'weights at step {ii+1} is: {weights}')
acc = accuracy(target, predictor(weights))
print(f'accuracy at step {ii+1} is: {acc}')
print('')
```

If you want help with diagnosing an error, please put the full error message below:

```
# there are no errors, but the code behavior is not desired.
```

`qml.about()`

output:

Name: PennyLane

Version: 0.35.1

Summary: PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

Home-page: GitHub - PennyLaneAI/pennylane: PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.

Author:

Author-email:

License: Apache License 2.0

Location: /usr/local/lib/python3.10/dist-packages

Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml, typing-extensions

Required-by: PennyLane_Lightning

Platform info: Linux-6.1.58±x86_64-with-glibc2.35

Python version: 3.10.12

Numpy version: 1.25.2

Scipy version: 1.11.4

Installed devices:

- default.clifford (PennyLane-0.35.1)
- default.gaussian (PennyLane-0.35.1)
- default.mixed (PennyLane-0.35.1)
- default.qubit (PennyLane-0.35.1)
- default.qubit.autograd (PennyLane-0.35.1)
- default.qubit.jax (PennyLane-0.35.1)
- default.qubit.legacy (PennyLane-0.35.1)
- default.qubit.tf (PennyLane-0.35.1)
- default.qubit.torch (PennyLane-0.35.1)
- default.qutrit (PennyLane-0.35.1)
- null.qubit (PennyLane-0.35.1)
- lightning.qubit (PennyLane_Lightning-0.35.1)