# PennyLane: Automatic differentiation of hybrid quantum-classical computations

arXiv: https://arxiv.org/abs/1811.04968 · HTML: https://mlsanity.com/paper/1811.04968/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 10 of 10)

- [Quantum-Inspired Machine Learning: a Survey](https://mlsanity.com/paper/2308.11269/) (2023)
- [Opportunities and limitations of explaining quantum machine learning](https://mlsanity.com/paper/2412.14753/) (2024)
- [Unclonability and Quantum Cryptanalysis: From Foundations to Applications](https://mlsanity.com/paper/2210.17545/) (2022)
- [Efficient Quantum Gradient and Higher-order Derivative Estimation via Generalized Hadamard Test](https://mlsanity.com/paper/2408.05406/) (2024)
- [An LLM System for Autonomous Variational Quantum Circuit Design](https://mlsanity.com/paper/2606.13380/) (2026)
- [Classical SU(2) Models Match or Exceed Shallow Variational Quantum Circuits on Vision Benchmarks](https://mlsanity.com/paper/2608.07822/) (2026)
- [When Quantum Meets AI: Quantum Methods for Machine Learning and Machine Learning Methods for Quantum Systems](https://mlsanity.com/paper/2609.25641/) (2026)
- [Quantum Entangled Multimodal Fusion Networks (QEMFN): Resource-Aware Hybrid Vision-Language Fusion via Trainable Entanglement](https://mlsanity.com/paper/2610.08216/) (2026)
- [Quantum anomaly detection in real scarce data](https://mlsanity.com/paper/2610.09635/) (2026)
- [QuSema: Detecting Silent Bugs in Quantum Libraries via Quantum-knowledge-enhanced Agents](https://mlsanity.com/paper/2610.10258/) (2026)

## Abstract (arXiv)

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. 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 hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.