¿Qué es PennyLane?
PennyLane es el framework de ML cuántico de código abierto de Xanadu. Trata los circuitos cuánticos como funciones diferenciables, lo que permite la optimización basada en gradientes directamente a través de cálculos cuánticos. Puedes calcular gradientes de circuitos cuánticos mediante reglas de desplazamiento de parámetros (parameter-shift), diferenciación adjunta o retropropagación, y conectarlos a bucles de entrenamiento de PyTorch o JAX. Todo esto es gratuito y local.
Instalación
terminal
# Core with default.qubit (pure NumPy, always free) pip install pennylane # Fast C++ simulator (10-100x speedup) pip install pennylane-lightning # GPU simulator (requires NVIDIA GPU) pip install pennylane-lightning-gpu # For JAX or PyTorch integration pip install pennylane jax jaxlib # JAX pip install pennylane torch # PyTorchPrimer circuito cuántico
pennylane_basic.py
import pennylane as qml import numpy as np # Choose your device (all free, local) dev = qml.device("default.qubit", wires=2) # dev = qml.device("lightning.qubit", wires=2) # Faster C++ version @qml.qnode(dev) def bell_state(): qml.Hadamard(wires=0) qml.CNOT(wires=[0, 1]) return qml.probs(wires=[0, 1]) result = bell_state() print(result) # [0.5, 0. , 0. , 0.5] # Draw the circuit print(qml.draw(bell_state)()) # 0: ──H─╭●──┤ ╭Probs # 1: ────╰X──┤ ╰ProbsAprendizaje automático cuántico — Clasificador variacional
qml_classifier.py
import pennylane as qml import numpy as np dev = qml.device("default.qubit", wires=2) @qml.qnode(dev) def variational_circuit(params, x): # Encode input data qml.AngleEmbedding(x, wires=[0, 1]) # Variational ansatz qml.BasicEntanglerLayers(params, wires=[0, 1]) return qml.expval(qml.PauliZ(0)) # Initialize random parameters params = np.random.uniform(0, np.pi, size=(3, 2)) # Compute gradient with parameter-shift rule (exact!) grad_fn = qml.grad(variational_circuit) x_sample = np.array([0.1, 0.2]) gradients = grad_fn(params, x_sample) print(f"Parameters shape: {params.shape}") print(f"Gradient shape: {gradients.shape}") # Training loop optimizer = qml.AdamOptimizer(stepsize=0.01) for step in range(100): params, cost = optimizer.step_and_cost( lambda p: variational_circuit(p, x_sample), params ) if step % 20 == 0: print(f"Step {step}: cost = {cost:.4f}")Uso del backend de JAX para mayor velocidad
pennylane_jax.py
import pennylane as qml import jax import jax.numpy as jnp dev = qml.device("default.qubit", wires=4) @qml.qnode(dev, interface="jax") def circuit(params): for i in range(4): qml.RY(params[i], wires=i) for i in range(3): qml.CNOT(wires=[i, i+1]) return qml.expval(qml.PauliZ(0) @ qml.PauliZ(3)) # JIT compile the circuit for massive speedup jit_circuit = jax.jit(circuit) # Automatic differentiation with JAX grad_circuit = jax.grad(jit_circuit) params = jnp.array([0.1, 0.2, 0.3, 0.4]) print(jit_circuit(params)) # Fast JIT-compiled execution print(grad_circuit(params)) # Automatic gradientConexión a otros backends
pennylane_backends.py
import pennylane as qml # Local simulators (all free) qml.device("default.qubit", wires=4) # NumPy qml.device("lightning.qubit", wires=4) # C++ (fast) qml.device("lightning.gpu", wires=4) # NVIDIA GPU # IBM Quantum (free tier — needs account) # pip install pennylane-qiskit qml.device("qiskit.ibmq", wires=4, backend="ibm_sherbrooke") # Amazon Braket # pip install amazon-braket-pennylane-plugin qml.device("braket.local.qubit", wires=4) # Free local qml.device("braket.aws.qubit", wires=4, device_arn="arn:aws:braket:::device/quantum-simulator/amazon/sv1")💡
Also available via HLQuantum
Want to run the same circuit on multiple backends without rewriting your code? HLQuantum abstracts this SDK (and 5 others) behind a single unified API.
python
import hlquantum as hlq qc = hlq.Circuit(2) qc.h(0).cx(0, 1).measure_all() # One line to switch between any backend result = hlq.run(qc, shots=1024) # auto-detect result = hlq.run(qc, shots=1024, backend="pennylane") # explicit