REVIEW 7 cited by
Hybrid quantum programming with PennyLane Lightning on HPC platforms
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Hybrid quantum programming with PennyLane Lightning on HPC platforms
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.
Forward citations
Cited by 7 Pith papers
-
Quantum Optimization for Electromagnetics: Physics-Informed QAOA for Reconfigurable Intelligent Surfaces
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.
-
Electronic Structure Calculations from Occupation Numbers on Quantum Computers
ON-VQE estimates molecular energies from quantum-measured occupation numbers alone, reducing VQE measurement settings to a single qubit-wise commuting group.
-
VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?
Compile-once PyTorch-native statevector simulation with native autograd yields large median speedups for static VQC inference and training, with an open selector for when to use it.
-
VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?
A compile-once PyTorch statevector backend for small static quantum circuits achieves 4.5-26.8x median GPU speedups over generic simulators and ships an oracle for when to use it.
-
Not Your Usual FFT: QFT$\rightarrow$FFT via Classical Quantum-Circuit Simulation
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.
-
How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits
A comprehensive review of scaling paths for superconducting quantum computers, with resource and sensitivity analyses for utility-scale applications under realistic error distributions.
-
Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking
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...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.