Graph VQE CUDA-Q Multi QPU Simulation Framework for Hamiltonian-Aware Protein Folding VQE

Jul 16, 2026

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Variational Quantum Eigensolver (VQE) enables ground-state energy estimation for protein folding and related molecular simulation, yet hardware noise and circuit/algorithmic limits constrain accuracy and scalability. NVIDIA CUDA-Q provides exact simulation but lacks Qiskit support and efficient parallelization. Graph-VQE extends CUDA-Q with optimization-level parallelism by partitioning Hamiltonian interaction graphs into weakly coupled clusters via Louvain detection, then refining globally using Hamiltonian batching and a custom Qiskit-CUDA-Q integration layer. Evaluations on protein folding show lower final energies, competitive RMSD and binding affinity, and stable performance across multi-GPU environments.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

Introduction

VQE background and limitations

Need for parallel optimization

Bottlenecks in parallel scaling

Graph-VQE motivation and contributions

Graph-VQE: A CUDA-Q Multi-QPU Simulation Framework for Hamiltonian-Aware Protein-Folding VQE arXiv :2607 .02749v 1 [ cs .ET] 2 Jul 2026 Yujun Feng Miami University Oxford, OH, United States [fengy46@miamioh.edu](fengy46@miamioh.edu) Shuai Xu Case Western Reserve University Cleveland, OH, United States [sxx214@case.edu](sxx214@case.edu) Yuqi Zhang Kent State University Kent, OH, United States [yzhan135@kent.edu](yzhan135@kent.edu) Jingyi Huang Miami University Oxford, OH, United States [huangj84@miamioh.edu](huangj84@miamioh.edu) Bo Fang University of Texas at Arlington Arlington, TX, United States [Bo.fang@uta.edu](Bo.fang@uta.edu) Qiang Guan Kent State University Kent, OH, United States [qguan@kent.edu](qguan@kent.edu) Yang Zhang Miami University Oxford, OH, United States [zhang981@miamioh.edu](zhang981@miamioh.edu) Abstract—The Variational Quantum Eigensolver (VQE) is essential for molecular simulation in drug discovery, but hardware noise and algorithmic limits restrict its precision. While the NVIDIA CUDA-Q platform mitigates some hardware issues via exact simulation, it lacks Qiskit support and restricts parallelization. To solve this, we introduce Graph-VQE, a novel framework that extends CUDA-Q with optimization-level parallelism. Graph-VQE leverages amino acid sequence structures by partitioning Hamiltonian interaction graphs into weakly coupled clusters using Louvain community detection. These clusters undergo restricted updates on the full-Hamiltonian objective, followed by a global refinement stage utilizing Hamiltonian batching. Furthermore, a custom Qiskit-CUDA-Q integration layer enables standard workflows with GPU acceleration. Evaluationson protein folding tasks prove that Graph-VQE outperforms baselines, achieving lower final energies. It delivers competitive RMSD and binding affinity compared to AlphaFold3 and IBM quantum processors while maintaining stable quality across multi-GPU environments, thereby providing a highly practical path toward high-fidelity biomolecular simulations. Index Terms—Variational quantum eigensolver, protein folding, hybrid quantum-classical computing, Hamiltonian partitioning, Louvain community detection, multi-QPU parallelization, CUDA-Q I. INTRODUCTION The Variational Quantum Eigensolver (VQE) is a leading hybrid quantum-classical algorithm for molecular simulation, enabling ground-state energy estimation critical for drug discovery, protein structure prediction, and materials design [1],[2] . However, current quantum hardware suffers from gate errors, decoherence, and limited circuit depth, preventing VQE from reaching the “chemical accuracy” (1 kcal/mol) required for reliable predictions [3]–[5] . Beyond hardware noise, standard VQE also faces an algorithmic bottleneck: the classical optimizer updates a single global parameter vector per iteration, and this sequential process becomes increasingly expensive as molecular systems grow, both because the num- ber of tunable parameters grows and the optimization landscape flattens at scale, making it increasingly difficult for the optimizer to find directions of improvement [6],[7] . Parallelizing the optimization itself, rather than only the measurement step, is therefore essential to scale VQE toward biologically relevant systems such as protein folding, where identifying the native state demands absolute energy minimization and failure produces misfoldings linked to diseases like Alzheimer’s and Parkinson’s [8], [9] . Fig. 1. Binding Affinity Comparison (kcal/mol, lower is better.) Scaling VQE for large-scale biological systems requires shifting toward parallel optimization, but several architectural bottlenecks currently stand in the way. First, breaking a quantum problem into parallel parts is difficult; simple geometric partitioning often splits strongly connected qubits, creating errors that ruin the final result [10], [11] . Second, distributing these connected parts across separa

FAQs

Why is VQE limited for reliable molecular simulation in practice?

VQE is constrained by hardware noise (gate errors and decoherence) and by algorithmic bottlenecks where classical optimization updates a single global parameter vector, which becomes harder as systems scale.

How does Graph-VQE improve scalability compared with using CUDA-Q alone?

Graph-VQE adds optimization-level parallelism to CUDA-Q, addressing the lack of Qiskit support and enabling parallel optimization beyond only parallel measurement.

What role does Louvain community detection play in Graph-VQE?

Graph-VQE partitions the Hamiltonian interaction graph derived from amino-acid sequence structure into weakly coupled clusters using Louvain detection, then performs restricted updates before a global refinement stage.

Jul 16, 2026

Upload by Aurora