PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for Strawberry Fields, Rigetti Forest, Qiskit, Cirq, and ProjectQ, allowing PennyLane optimizations to be run on publicly accessible quantum devices provided by Rigetti and IBM Q. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, and autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.

中文翻译：

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PennyLane：混合量子经典计算的自动微分

PennyLane 是一个 Python 3 软件框架，用于优化和机器学习量子计算和混合量子经典计算。该库为近期量子计算设备提供了统一的架构，支持量子位和连续变量范式。PennyLane 的核心功能是能够以与经典技术（如反向传播）兼容的方式计算变分量子电路的梯度。因此，PennyLane 将优化和机器学习中常见的自动微分算法扩展到包括量子和混合计算。插件系统使框架与任何基于门的量子模拟器或硬件兼容。我们为 Strawberry Fields、Rigetti Forest、Qiskit、Cirq 和 ProjectQ 提供插件，允许在 Rigetti 和 IBM Q 提供的可公开访问的量子设备上运行 PennyLane 优化。在经典方面，PennyLane 与加速机器学习库（如 TensorFlow、PyTorch 和 autograd）接口。PennyLane 可用于变分量子特征求解器的优化、量子近似优化、量子机器学习模型和许多其他应用。