Hybrid quantum programming with PennyLane Lightning on HPC platforms
Not on the map. Why?
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Mar 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 2; 2025: 3; 2026: 4.
9 papers.
- 2025Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learningcited by 2
- 2024Quantum Re-Uploading for Calorimetry: Optimized Architectures with Extended Expressivitycited by 1
- 2024Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classificationcited by 0
- 2025QuIRK: Quantum-Inspired Re-uploading KANcited by 0
- 2025TorchQuantumDistributedcited by 0
- 2026Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communicationcited by 0
- 2026The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discoverycited by 0
- 2026VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?cited by 0
- 2026HMB-GAN: Hybrid Multi-B'ezier GAN for Vector Shape Synthesiscited by 0
- 2026HMB-GAN: Hybrid Multi-B'ezier GAN for Vector Shape Synthesis17 Sep 2026
- 2026VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?13 Jul 2026
- 2026The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery18 Mar 2026
- 2026Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communication26 Jan 2026
- 2025TorchQuantumDistributed24 Nov 2025
- 2025QuIRK: Quantum-Inspired Re-uploading KAN9 Oct 2025
- 2025Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning11 Jul 2025
- 2024Quantum Re-Uploading for Calorimetry: Optimized Architectures with Extended Expressivity16 Dec 2024
- 2024Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification30 Apr 2024