pith. sign in

arxiv: 2403.02512 · v1 · pith:S4OPQ2DLnew · submitted 2024-03-04 · 🪐 quant-ph · cs.DC· cs.ET· physics.comp-ph

Hybrid quantum programming with PennyLane Lightning on HPC platforms

classification 🪐 quant-ph cs.DCcs.ETphysics.comp-ph
keywords lightningmultipleperformancequantumacrossarchitecturesgpushigh-performance
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Quantum Optimization for Electromagnetics: Physics-Informed QAOA for Reconfigurable Intelligent Surfaces

    cs.CE 2026-05 unverdicted novelty 6.0

    Sparse distance-penalized Ising models are required for feasible QAOA execution on NISQ devices when optimizing RIS with mutual coupling, at the cost of reduced beamforming precision compared to dense models.

  2. Not Your Usual FFT: QFT$\rightarrow$FFT via Classical Quantum-Circuit Simulation

    cs.ET 2026-06 unverdicted novelty 4.0

    QFT→FFT computes DFT via classical QFT circuit simulation on qsim with AVX/CUDA backends, claiming parity or better performance than FFTW on CPU/GPU plus an approximate variant.

  3. How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

    quant-ph 2024-11 accept novelty 4.0

    A comprehensive review of scaling paths for superconducting quantum computers, with resource and sensitivity analyses for utility-scale applications under realistic error distributions.

  4. Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking

    quant-ph 2026-04 unverdicted novelty 3.0

    Hybriqu Encoder delivers 5.4% faster pure angle encoding at 64 qubits on Apple Silicon by using AVX SIMD and cache-friendly precalculations, with gains increasing beyond L1 cache size while full-state updates remain m...