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PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

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Quick Overview

PennyLane is an open-source software framework for quantum machine learning, quantum computing, and quantum chemistry. It provides a way to train quantum circuits using automatic differentiation and integrates with popular machine learning libraries like PyTorch and TensorFlow. PennyLane enables users to easily design and optimize quantum algorithms on various quantum hardware and simulators.

Pros

  • Seamless integration with classical machine learning frameworks
  • Support for multiple quantum hardware providers and simulators
  • Extensive library of built-in quantum operations and templates
  • Active development and community support

Cons

  • Steep learning curve for those new to quantum computing
  • Performance can be limited by the underlying quantum hardware
  • Documentation can be overwhelming for beginners
  • Some advanced features may require in-depth quantum knowledge

Code Examples

  1. Creating a simple quantum circuit:
import pennylane as qml

dev = qml.device('default.qubit', wires=2)

@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))

result = circuit([0.1, 0.2])
print(result)
  1. Optimizing a variational quantum circuit:
import pennylane as qml
import pennylane.numpy as np

dev = qml.device('default.qubit', wires=2)

@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))

def cost(params):
    return circuit(params)

opt = qml.GradientDescentOptimizer(stepsize=0.4)
params = np.array([0.011, 0.012], requires_grad=True)

for i in range(100):
    params = opt.step(cost, params)

print(f"Optimized parameters: {params}")
  1. Quantum machine learning with PyTorch:
import pennylane as qml
import torch
from torch.autograd import Variable

n_qubits = 4
dev = qml.device("default.qubit", wires=n_qubits)

@qml.qnode(dev, interface="torch")
def quantum_circuit(inputs, weights):
    qml.templates.AngleEmbedding(inputs, wires=range(n_qubits))
    qml.templates.StronglyEntanglingLayers(weights, wires=range(n_qubits))
    return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]

layer_1 = torch.nn.Linear(4, 8)
layer_2 = torch.nn.Linear(8, 4)
weights = Variable(torch.randn(3, n_qubits, 3))

def hybrid_model(x):
    x = torch.relu(layer_1(x))
    x = torch.relu(layer_2(x))
    x = quantum_circuit(x, weights)
    return torch.sum(x)

x = Variable(torch.randn(4))
output = hybrid_model(x)
print(f"Model output: {output}")

Getting Started

To get started with PennyLane, follow these steps:

  1. Install PennyLane and its dependencies:

    pip install pennylane

  2. Import PennyLane in your Python script:

    import pennylane as qml

  3. Create a quantum device:

    dev = qml.device('default.qubit', wires=2)

  4. Define a quantum circuit using the @qml.qnode decorator:

Competitor Comparisons

Qiskit logo

qiskit

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Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.

Pros of Qiskit

  • More extensive hardware support, especially for IBM quantum devices
  • Larger community and ecosystem, with more resources and tutorials available
  • Advanced features for quantum circuit optimization and error mitigation

Cons of Qiskit

  • Steeper learning curve for beginners
  • Less flexibility in terms of backend integration compared to PennyLane
  • More complex installation process with multiple components

Code Comparison

Qiskit:

from qiskit import QuantumCircuit, execute, Aer

qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure([0, 1], [0, 1])

backend = Aer.get_backend('qasm_simulator')
job = execute(qc, backend, shots=1000)
result = job.result()

PennyLane:

import pennylane as qml

dev = qml.device('default.qubit', wires=2)

@qml.qnode(dev)
def circuit():
    qml.Hadamard(wires=0)
    qml.CNOT(wires=[0, 1])
    return qml.probs(wires=[0, 1])

result = circuit()

Both frameworks offer powerful tools for quantum computing, with Qiskit providing more extensive hardware support and advanced features, while PennyLane offers greater flexibility and ease of use for beginners. The choice between them depends on specific project requirements and user preferences.

quantumlib logo

Cirq

4,228

A Python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.

Pros of Cirq

  • More comprehensive set of quantum gates and operations
  • Better support for noise simulation and error mitigation
  • Stronger integration with Google's quantum hardware

Cons of Cirq

  • Steeper learning curve for beginners
  • Less focus on hybrid quantum-classical algorithms
  • More limited support for automatic differentiation

Code Comparison

Cirq example:

import cirq

q0, q1 = cirq.LineQubit.range(2)
circuit = cirq.Circuit(
    cirq.H(q0),
    cirq.CNOT(q0, q1),
    cirq.measure(q0, q1, key='result')
)

PennyLane example:

import pennylane as qml

dev = qml.device('default.qubit', wires=2)

@qml.qnode(dev)
def circuit():
    qml.Hadamard(wires=0)
    qml.CNOT(wires=[0, 1])
    return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))

Both libraries offer quantum circuit creation and simulation, but Cirq focuses more on low-level circuit manipulation, while PennyLane emphasizes variational quantum algorithms and gradient-based optimization.

rigetti logo

pyquil

1,396

A Python library for quantum programming using Quil.

Pros of pyquil

  • Specialized for Rigetti quantum hardware, offering deep integration and optimization
  • Provides low-level control over quantum operations and pulse-level programming
  • Includes tools for noise characterization and mitigation specific to Rigetti devices

Cons of pyquil

  • Limited to Rigetti hardware, less flexible for other quantum platforms
  • Steeper learning curve due to lower-level abstractions
  • Smaller ecosystem and community compared to PennyLane

Code Comparison

pyquil:

from pyquil import Program, get_qc
from pyquil.gates import H, CNOT

p = Program()
p += H(0)
p += CNOT(0, 1)
qc = get_qc('2q-qvm')
result = qc.run_and_measure(p, trials=1000)

PennyLane:

import pennylane as qml

dev = qml.device('default.qubit', wires=2)

@qml.qnode(dev)
def circuit():
    qml.Hadamard(wires=0)
    qml.CNOT(wires=[0, 1])
    return qml.probs(wires=[0, 1])

result = circuit()

Both examples create a simple Bell state, but PennyLane's approach is more abstracted and device-agnostic, while pyquil's is more hardware-specific and lower-level.

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README

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry.

Train a quantum computer the same way as a neural network.

Key Features

  • Machine learning on quantum hardware. Connect to quantum hardware using PyTorch, TensorFlow, JAX, Keras, or NumPy. Build rich and flexible hybrid quantum-classical models.

  • Just in time compilation. Experimental support for just-in-time compilation. Compile your entire hybrid workflow, with support for advanced features such as adaptive circuits, real-time measurement feedback, and unbounded loops. See Catalyst for more details.

  • Device-independent. Run the same quantum circuit on different quantum backends. Install plugins to access even more devices, including Strawberry Fields, Amazon Braket, IBM Q, Google Cirq, Rigetti Forest, Qulacs, Pasqal, Honeywell, and more.

  • Follow the gradient. Hardware-friendly automatic differentiation of quantum circuits.

  • Batteries included. Built-in tools for quantum machine learning, optimization, and quantum chemistry. Rapidly prototype using built-in quantum simulators with backpropagation support.

Installation

PennyLane requires Python version 3.10 and above. Installation of PennyLane, as well as all dependencies, can be done using pip:

python -m pip install pennylane

Docker support

Docker support exists for building using CPU and GPU (Nvidia CUDA 11.1+) images. See a more detailed description here.

Getting started

For an introduction to quantum machine learning, guides and resources are available on PennyLane's quantum machine learning hub:

You can also check out our documentation for quickstart guides to using PennyLane, and detailed developer guides on how to write your own PennyLane-compatible quantum device.

Tutorials and demonstrations

Take a deeper dive into quantum machine learning by exploring cutting-edge algorithms on our demonstrations page.

All demonstrations are fully executable, and can be downloaded as Jupyter notebooks and Python scripts.

If you would like to contribute your own demo, see our demo submission guide.

Videos

Seeing is believing! Check out our videos to learn about PennyLane, quantum computing concepts, and more.

Contributing to PennyLane

We welcome contributions—simply fork the PennyLane repository, and then make a pull request containing your contribution. All contributors to PennyLane will be listed as authors on the releases. All users who contribute significantly to the code (new plugins, new functionality, etc.) will be listed on the PennyLane arXiv paper.

We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects or applications built on PennyLane.

See our contributions page and our developer hub for more details.

Support

If you are having issues, please let us know by posting the issue on our GitHub issue tracker.

We also have a PennyLane discussion forum—come join the community and chat with the PennyLane team.

Note that we are committed to providing a friendly, safe, and welcoming environment for all. Please read and respect the Code of Conduct.

Authors

PennyLane is the work of many contributors.

If you are doing research using PennyLane, please cite our paper:

Ville Bergholm et al. PennyLane: Automatic differentiation of hybrid quantum-classical computations. 2018. arXiv:1811.04968

License

PennyLane is free and open source, released under the Apache License, Version 2.0.