Accelerating Parameter Initialization in Quantum Chemical Simulations via LSTM-FC-VQE

Abstract

We present a meta-learning framework that leverages Long Short-Term Memory (LSTM) neural networks to accelerate parameter initialization in quantum chemical simulations using the Variational Quantum Eigensolver (VQE). By training the LSTM on optimized parameters from small molecules, the model learns to predict high-quality initializations for larger systems, reducing the number of required VQE iterations. Our enhanced LSTM-FC-VQE architecture introduces a fully connected layer, improving adaptability across molecules with varying parameter sizes. Experimental results show that our approach achieves faster convergence and lower energy errors than traditional initialization, demonstrating its practical potential for efficient quantum simulations in the NISQ era.