pennylane

name

pennylane

description

Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.

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(, wires=)



 ():
    qml.RX(params[], wires=)
    qml.RY(params[], wires=)
    qml.CNOT(wires=[, ])
     qml.expval(qml.PauliZ())


opt = qml.GradientDescentOptimizer(stepsize=)
params = np.array([, ], requires_grad=)

 i  ():
    params = opt.step(circuit, params)