alterlab-pennylane

PennyLane

Overview

PennyLane is a quantum computing library that enables training quantum computers like neural networks. It provides automatic differentiation of quantum circuits, device-independent programming, and seamless integration with classical machine learning frameworks.

Installation

Install using uv:

uv pip install pennylane

For quantum hardware access, install device plugins:

# IBM Quantum
uv pip install pennylane-qiskit

# Amazon Braket
uv pip install amazon-braket-pennylane-plugin

# Google Cirq
uv pip install pennylane-cirq

# Rigetti Forest
uv pip install pennylane-rigetti

# IonQ
uv pip install pennylane-ionq

Quick Start

Build a quantum circuit and optimize its parameters:

import pennylane as qml
from pennylane import numpy as np

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

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

# Optimize parameters
opt = qml.GradientDescentOptimizer(stepsize=0.1)
params = np.array([0.1, 0.2], requires_grad=True)

for i in range(100):
    params = opt.step(circuit, params)

Core Capabilities

New to PennyLane? See references/getting_started.md for installation, QNodes, devices, gradients, and a first optimization loop.

1. Quantum Circuit Construction

Build circuits with gates, measurements, and state preparation. See references/quantum_circuits.md for:

• Single and multi-qubit gates
• Controlled operations and conditional logic
• Mid-circuit measurements and adaptive circuits
• Various measurement types (expectation, probability, samples)
• Circuit inspection and debugging

2. Quantum Machine Learning

Create hybrid quantum-classical models. See references/quantum_ml.md for:

• Integration with PyTorch, JAX, TensorFlow
• Quantum neural networks and variational classifiers
• Data encoding strategies (angle, amplitude, basis, IQP)
• Training hybrid models with backpropagation
• Transfer learning with quantum circuits