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pennylane by k-dense-ai/claude-scientific-skills
npx skills add https://github.com/k-dense-ai/claude-scientific-skills --skill pennylanePennyLane 是一个量子计算库,能够像训练神经网络一样训练量子计算机。它提供量子电路的自动微分、设备无关的编程,以及与经典机器学习框架的无缝集成。
使用 uv 安装:
uv pip install pennylane
如需访问量子硬件,请安装设备插件:
# 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
构建量子电路并优化其参数:
import pennylane as qml
from pennylane import numpy as np
# 创建设备
dev = qml.device('default.qubit', wires=2)
# 定义量子电路
@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))
# 优化参数
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)
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使用门、测量和状态准备构建电路。请参阅 references/quantum_circuits.md 了解:
创建混合量子-经典模型。请参阅 references/quantum_ml.md 了解:
模拟分子并计算基态能量。请参阅 references/quantum_chemistry.md 了解:
在模拟器或量子硬件上执行。请参阅 references/devices_backends.md 了解:
使用各种优化器训练量子电路。请参阅 references/optimization.md 了解:
利用模板、变换和编译。请参阅 references/advanced_features.md 了解:
# 1. 定义拟设
@qml.qnode(dev)
def classifier(x, weights):
# 编码数据
qml.AngleEmbedding(x, wires=range(4))
# 变分层
qml.StronglyEntanglingLayers(weights, wires=range(4))
return qml.expval(qml.PauliZ(0))
# 2. 训练
opt = qml.AdamOptimizer(stepsize=0.01)
weights = np.random.random((3, 4, 3)) # 3 层, 4 个线路
for epoch in range(100):
for x, y in zip(X_train, y_train):
weights = opt.step(lambda w: (classifier(x, w) - y)**2, weights)
from pennylane import qchem
# 1. 构建哈密顿量
symbols = ['H', 'H']
coords = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.74])
H, n_qubits = qchem.molecular_hamiltonian(symbols, coords)
# 2. 定义拟设
@qml.qnode(dev)
def vqe_circuit(params):
qml.BasisState(qchem.hf_state(2, n_qubits), wires=range(n_qubits))
qml.UCCSD(params, wires=range(n_qubits))
return qml.expval(H)
# 3. 优化
opt = qml.AdamOptimizer(stepsize=0.1)
params = np.zeros(10, requires_grad=True)
for i in range(100):
params, energy = opt.step_and_cost(vqe_circuit, params)
print(f"Step {i}: Energy = {energy:.6f} Ha")
# 相同的电路,不同的后端
circuit_def = lambda dev: qml.qnode(dev)(circuit_function)
# 在模拟器上测试
dev_sim = qml.device('default.qubit', wires=4)
result_sim = circuit_def(dev_sim)(params)
# 在量子硬件上运行
dev_hw = qml.device('qiskit.ibmq', wires=4, backend='ibmq_manila')
result_hw = circuit_def(dev_hw)(params)
如需全面了解特定主题,请查阅参考文件:
references/getting_started.md - 安装、基本概念、第一步references/quantum_circuits.md - 门、测量、电路模式references/quantum_ml.md - 混合模型、框架集成、量子神经网络references/quantum_chemistry.md - VQE、分子哈密顿量、化学工作流references/devices_backends.md - 模拟器、硬件插件、设备配置references/optimization.md - 优化器、梯度、变分算法references/advanced_features.md - 模板、变换、JIT 编译、噪声default.qubit 上测试qml.specs() 分析电路复杂度每周安装量
55
代码库
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17.3K
首次出现
2026年1月20日
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安装于
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gemini-cli46
claude-code44
cursor44
github-copilot43
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.
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
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)
Build circuits with gates, measurements, and state preparation. See references/quantum_circuits.md for:
Create hybrid quantum-classical models. See references/quantum_ml.md for:
Simulate molecules and compute ground state energies. See references/quantum_chemistry.md for:
Execute on simulators or quantum hardware. See references/devices_backends.md for:
Train quantum circuits with various optimizers. See references/optimization.md for:
Leverage templates, transforms, and compilation. See references/advanced_features.md for:
# 1. Define ansatz
@qml.qnode(dev)
def classifier(x, weights):
# Encode data
qml.AngleEmbedding(x, wires=range(4))
# Variational layers
qml.StronglyEntanglingLayers(weights, wires=range(4))
return qml.expval(qml.PauliZ(0))
# 2. Train
opt = qml.AdamOptimizer(stepsize=0.01)
weights = np.random.random((3, 4, 3)) # 3 layers, 4 wires
for epoch in range(100):
for x, y in zip(X_train, y_train):
weights = opt.step(lambda w: (classifier(x, w) - y)**2, weights)
from pennylane import qchem
# 1. Build Hamiltonian
symbols = ['H', 'H']
coords = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.74])
H, n_qubits = qchem.molecular_hamiltonian(symbols, coords)
# 2. Define ansatz
@qml.qnode(dev)
def vqe_circuit(params):
qml.BasisState(qchem.hf_state(2, n_qubits), wires=range(n_qubits))
qml.UCCSD(params, wires=range(n_qubits))
return qml.expval(H)
# 3. Optimize
opt = qml.AdamOptimizer(stepsize=0.1)
params = np.zeros(10, requires_grad=True)
for i in range(100):
params, energy = opt.step_and_cost(vqe_circuit, params)
print(f"Step {i}: Energy = {energy:.6f} Ha")
# Same circuit, different backends
circuit_def = lambda dev: qml.qnode(dev)(circuit_function)
# Test on simulator
dev_sim = qml.device('default.qubit', wires=4)
result_sim = circuit_def(dev_sim)(params)
# Run on quantum hardware
dev_hw = qml.device('qiskit.ibmq', wires=4, backend='ibmq_manila')
result_hw = circuit_def(dev_hw)(params)
For comprehensive coverage of specific topics, consult the reference files:
references/getting_started.md - Installation, basic concepts, first stepsreferences/quantum_circuits.md - Gates, measurements, circuit patternsreferences/quantum_ml.md - Hybrid models, framework integration, QNNsreferences/quantum_chemistry.md - VQE, molecular Hamiltonians, chemistry workflowsreferences/devices_backends.md - Simulators, hardware plugins, device configurationreferences/optimization.md - Optimizers, gradients, variational algorithmsreferences/advanced_features.md - Templates, transforms, JIT compilation, noisedefault.qubit before deploying to hardwareqml.specs() to analyze circuit complexityWeekly Installs
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