# Hybrid quantum programming with PennyLane Lightning on HPC platforms

arXiv: https://arxiv.org/abs/2403.02512 · HTML: https://mlsanity.com/paper/2403.02512/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2024.

## Built on by (most cited first, 9 of 9)

- [Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning](https://mlsanity.com/paper/2507.08746/) (2025)
- [Quantum Re-Uploading for Calorimetry: Optimized Architectures with Extended Expressivity](https://mlsanity.com/paper/2412.12397/) (2024)
- [VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?](https://mlsanity.com/paper/2607.11985/) (2026)
- [Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification](https://mlsanity.com/paper/2405.00156/) (2024)
- [QuIRK: Quantum-Inspired Re-uploading KAN](https://mlsanity.com/paper/2510.08650/) (2025)
- [TorchQuantumDistributed](https://mlsanity.com/paper/2511.19291/) (2025)
- [Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communication](https://mlsanity.com/paper/2601.18419/) (2026)
- [The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery](https://mlsanity.com/paper/2603.17790/) (2026)
- [HMB-GAN: Hybrid Multi-B'ezier GAN for Vector Shape Synthesis](https://mlsanity.com/paper/2609.21158/) (2026)

## Abstract (arXiv)

We introduce PennyLane's Lightning suite, a collection of high-performance state-vector simulators targeting CPU, GPU, and HPC-native architectures and workloads. Quantum applications such as QAOA, VQE, and synthetic workloads are implemented to demonstrate the supported classical computing architectures and showcase the scale of problems that can be simulated using our tooling. We benchmark the performance of Lightning with backends supporting CPUs, as well as NVidia and AMD GPUs, and compare the results to other commonly used high-performance simulator packages, demonstrating where Lightning's implementations give performance leads. We show improved CPU performance by employing explicit SIMD intrinsics and multi-threading, batched task-based execution across multiple GPUs, and distributed forward and gradient-based quantum circuit executions across multiple nodes. Our data shows we can comfortably simulate a variety of circuits, giving examples with up to 30 qubits on a single device or node, and up to 41 qubits using multiple nodes.