Parallelizing the Variational Quantum Eigensolver: From JIT Compilation to Multi-GPU Scaling
- URL: http://arxiv.org/abs/2601.09951v1
- Date: Thu, 15 Jan 2026 00:21:51 GMT
- Title: Parallelizing the Variational Quantum Eigensolver: From JIT Compilation to Multi-GPU Scaling
- Authors: Rylan Malarchick, Ashton Steed,
- Abstract summary: Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm for computing ground state energies of molecular systems.<n>We implement VQE to calculate the potential energy surface of the hydrogen molecule across 100 bond lengths using the PennyLane quantum computing framework.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm for computing ground state energies of molecular systems. We implement VQE to calculate the potential energy surface of the hydrogen molecule (H$_2$) across 100 bond lengths using the PennyLane quantum computing framework on an HPC cluster featuring 4$\times$ NVIDIA H100 GPUs (80GB each). We present a comprehensive parallelization study with four phases: (1) Optimizer + JIT compilation achieving 4.13$\times$ speedup, (2) GPU device acceleration achieving 3.60$\times$ speedup at 4 qubits scaling to 80.5$\times$ at 26 qubits, (3) MPI parallelization achieving 28.5$\times$ speedup, and (4) Multi-GPU scaling achieving 3.98$\times$ speedup with 99.4% parallel efficiency across 4 H100 GPUs. The combined effect yields 117$\times$ total speedup for the H$_2$ potential energy surface (593.95s $\rightarrow$ 5.04s). We conduct a CPU vs GPU scaling study from 4--26 qubits, finding GPU advantage at all scales with speedups ranging from 10.5$\times$ to 80.5$\times$. Multi-GPU benchmarks demonstrate near-perfect scaling with 99.4% efficiency and establish that a single H100 can simulate up to 29 qubits before hitting memory limits. The optimized implementation reduces runtime from nearly 10 minutes to 5 seconds, enabling interactive quantum chemistry exploration.
Related papers
- GaDE -- GPU-acceleration of time-dependent Dirac Equation for exascale [0.0]
GaDE is designed to simulate the electron dynamics in atoms induced by electromagnetic fields in the relativistic regime.<n>We evaluate GaDE on the pre-exascale supercomputer LUMI, powered by AMD MI250X GPUs and Hewlett-Packard's Slingshot interconnect.
arXiv Detail & Related papers (2025-12-25T14:47:36Z) - Minute-Long Videos with Dual Parallelisms [57.22737565366549]
Diffusion Transformer (DiT)-based video diffusion models generate high-quality videos at scale but incur prohibitive processing latency and memory costs for long videos.<n>We propose a novel distributed inference strategy, termed DualParal.<n>Instead of generating an entire video on a single GPU, we parallelize both temporal frames and model layers across GPUs.
arXiv Detail & Related papers (2025-05-27T11:55:22Z) - GPU-accelerated Effective Hamiltonian Calculator [70.12254823574538]
We present numerical techniques inspired by Nonperturbative Analytical Diagonalization (NPAD) and the Magnus expansion for the efficient calculation of effective Hamiltonians.<n>Our numerical techniques are available as an open-source Python package, $rm qCH_eff$.
arXiv Detail & Related papers (2024-11-15T06:33:40Z) - MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models [58.3342517278868]
This paper describes the design of Mixed-precision AutoRegressive LINear kernels.
It shows that batchsizes up to 16-32 can be supported with close to maximum ($4times$) quantization speedup.
MarLIN accomplishes this via a combination of techniques, such as asynchronous memory access, complex task scheduling and pipelining.
arXiv Detail & Related papers (2024-08-21T16:10:41Z) - Advanced Techniques for High-Performance Fock Matrix Construction on GPU Clusters [0.0]
opt-UM and opt-Brc introduce significant enhancements to Hartree-Fock caculations up to $f$-type angular momentum functions.
Opt-Brc excels for smaller systems and for highly contracted triple-$zeta$ basis sets, while opt-UM is advantageous for large molecular systems.
arXiv Detail & Related papers (2024-07-31T08:49:06Z) - Multi-GPU RI-HF Energies and Analytic Gradients $-$ Towards High Throughput Ab Initio Molecular Dynamics [0.0]
This article presents an optimized algorithm and implementation for calculating resolution-of-the-identity Hartree-Fock energies and analytic gradients using multiple Graphics Processing Units (GPUs)
The algorithm is especially designed for high throughput emphab initio molecular dynamics simulations of small and medium size molecules (10-100 atoms)
arXiv Detail & Related papers (2024-07-29T00:14:10Z) - PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine [68.8204255655161]
Support Vector Machines (SVMs) are widely used in machine learning.
However, even modern and optimized implementations do not scale well for large non-trivial dense data sets on cutting-edge hardware.
PLSSVM can be used as a drop-in replacement for an LVM.
arXiv Detail & Related papers (2022-02-25T13:24:23Z) - Simulation of quantum many-body dynamics with Tensor Processing Units:
Floquet prethermalization [0.3078264203938486]
We demonstrate the usage of TPUs for massively parallel, classical simulations of quantum many-body dynamics on long timescales.
We simulate the dynamics of L=34 qubits for over $105$ Floquet periods, corresponding to circuits with millions of two-qubit gates.
Our work demonstrates that TPUs can offer significant advantages for state-of-the-art simulations of quantum many-body dynamics.
arXiv Detail & Related papers (2021-11-15T19:02:54Z) - AxoNN: An asynchronous, message-driven parallel framework for
extreme-scale deep learning [1.5301777464637454]
AxoNN is a parallel deep learning framework that exploits asynchrony and message-driven execution to schedule neural network operations on each GPU.
By using the CPU memory as a scratch space for offloading data periodically during training, AxoNN is able to reduce GPU memory consumption by four times.
arXiv Detail & Related papers (2021-10-25T14:43:36Z) - Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous
Multi-GPU Servers [65.60007071024629]
We show that Adaptive SGD outperforms four state-of-the-art solutions in time-to-accuracy.
We show experimentally that Adaptive SGD outperforms four state-of-the-art solutions in time-to-accuracy.
arXiv Detail & Related papers (2021-10-13T20:58:15Z) - Efficient and Generic 1D Dilated Convolution Layer for Deep Learning [52.899995651639436]
We introduce our efficient implementation of a generic 1D convolution layer covering a wide range of parameters.
It is optimized for x86 CPU architectures, in particular, for architectures containing Intel AVX-512 and AVX-512 BFloat16 instructions.
We demonstrate the performance of our optimized 1D convolution layer by utilizing it in the end-to-end neural network training with real genomics datasets.
arXiv Detail & Related papers (2021-04-16T09:54:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.