pennylanelisted
Install: claude install-skill Yuuqq/research-grade-skills
# 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:
```bash
uv pip install pennylane
```
For quantum hardware access, install device plugins:
```bash
# 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:
```python
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
### 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-cir