pennylane

Erwartetes Ergebnis:

Here's a simple PennyLane circuit:

```python
import pennylane as qml
from pennylane import numpy as np

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

# Define the circuit
@qml.qnode(dev)
def circuit(params):
qml.RX(params[0], wires=0) # Rotate qubit 0
qml.RY(params[1], wires=1) # Rotate qubit 1
qml.CNOT(wires=[0, 1]) # Entangle qubits
return qml.expval(qml.PauliZ(0))

# Run the circuit
params = np.array([0.5, 0.3])
result = circuit(params)
```

Erwartetes Ergebnis:

PennyLane supports several encoding strategies:

1. **Angle Encoding** - Encode N features into N qubits using rotation angles
2. **Amplitude Encoding** - Encode 2^N features into N qubits using state amplitudes (efficient for N<~10)
3. **Basis State Encoding** - Encode binary features as computational basis states
4. **IQP Encoding** - Embed features in an IQP circuit for kernel-based approaches

Each encoding has tradeoffs between qubit efficiency and expressibility.