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

2026-07-02

AI Summary

This work addresses the limitations of the Variational Quantum Eigensolver (VQE) in protein folding simulations—namely hardware noise, algorithmic accuracy, and parallelization inefficiency—and the lack of Qiskit support in CUDA-Q. The authors propose Graph-VQE, a novel framework that introduces, for the first time within CUDA-Q, a Hamiltonian-aware graph partitioning strategy for parallel execution. By applying Louvain community detection to decompose the Hamiltonian interaction graph into weakly coupled clusters, the method integrates localized parameter updates with global Hamiltonian batching. Furthermore, a Qiskit-CUDA-Q adapter layer is developed to enable seamless integration with standard quantum workflows. This approach substantially enhances multi-GPU scalability, achieves lower final-state energies in protein folding tasks, and yields RMSD and binding affinity metrics comparable to those of AlphaFold3 and IBM quantum processors, all while maintaining consistently high-quality outputs.

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. Evaluations on 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.

Problem

• Variational Quantum Eigensolver
• Protein Folding
• Hamiltonian Simulation
• Multi-QPU Simulation
• Biomolecular Simulation

Innovation

• Graph-VQE
• Hamiltonian-aware partitioning
• Louvain community detection
• Qiskit-CUDA-Q integration
• multi-GPU VQE