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: ```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...