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

Abstract

Variational Quantum Eigensolver (VQE) is a leading algorithm for quantum chemistry on near-term devices, but its performance is often limited by inefficient parameter initialization, which leads to slow convergence and high computational overhead. While meta-learning approaches such as LSTM-VQE have been proposed to predict optimization trajectories, their applicability remains restricted by rigid ansatz structures and molecule-specific parameter dimensions. To address these limitations, we propose LSTM-FC-VQE, an enhanced meta-learning framework that combines Long Short-Term Memory (LSTM) networks with a fully connected projection layer. This design enables the model to standardize variable-length parameter inputs, allowing it to handle molecular systems with heterogeneous qubit counts and ansatz architectures more effectively. We validate our approach on benchmark molecules, including H4 and H2O, and demonstrate that LSTM-FC-VQE significantly reduces the number of VQE iterations required for convergence while achieving energy accuracies comparable to full configuration interaction (FCI) results. Beyond accelerating convergence, our method generalizes to unseen molecular configurations with minimal training data, offering a scalable and data-efficient strategy for quantum chemical simulations in the NISQ era.