Meta-Learning for GPU-Accelerated Quantum Many-Body Problems

TL;DR

This paper presents a GPU-accelerated meta-learning framework that enhances the efficiency and scalability of variational quantum algorithms for quantum chemistry and physics applications, achieving high accuracy and significant speedups.

Contribution

It introduces a novel LSTM-FC meta-initialization method integrated with GPU-accelerated quantum simulation, extending VQE's practical applicability across multiple quantum domains.

Findings

Achieves near FCI accuracy in molecular ground-state energy prediction.

Reproduces ground and excited states in quantum harmonic oscillators.

Demonstrates significant GPU speedups over CPU implementations.

Abstract

We explore the industrial and scientific applicability of the VQE-LSTM framework by integrating meta-learning with GPU accelerated quantum simulation using NVIDIA's CUDA-Q (CUDAQ) platform. This work demonstrates how an LSTM-FC meta-initialization module can extend the practical reach of the Variational Quantum Eigensolver (VQE) in both chemistry and physics domains. In the chemical regime, the framework predicts ground-state energies of molecular Hamiltonians derived from PySCF, achieving near FCI accuracy while maintaining favorable O(N^2) scaling with molecular size. In the physical counterpart, we applied the same model to quantized Simple Harmonic Motion systems (SHM), successfully reproducing its ground and excited states through VQE and Variational Quantum Deflation (VQD) methods. Benchmark results on NVIDIA GPUs reveal significant speedups over CPU-based implementations, validating CUDAQ's capability to handle large-scale variational workloads efficiently. Overall, this study establishes VQE-LSTM as a viable and scalable approach for GPU accelerated quantum simulation, bridging quantum chemistry and condensed-matter physics through a unified, meta-learned initialization strategy.