abstract
- We present a novel quantum generative framework based on Quantum Long Short-Term Memory (QLSTM) networks to optimize the sampling of Slater determinants in Full Configuration Interaction (FCI), a key method in quantum chemistry for solving the Schrödinger equation. Our model employs variational quantum circuits to learn an approximate distribution over the configuration space, guiding the generation of new, physically meaningful determinants. This distribution reflects the correlations among significant electronic configurations, allowing for a guided exploration of the determinant space and accelerating convergence toward reference energies. By framing determinant selection as a generative modeling task, our approach contributes to the emerging field of quantum generative AI applied to many-body systems. Implemented using Pennylane and accelerated with JAX, the QLSTM model converges faster than both classical LSTM and random baselines. Notably, it exhibits stable and consistent convergence across different hyperparameter settings, suggesting that the model captures an underlying physical structure rather than overfitting to specific training conditions. Our results demonstrate the scalability and physical consistency of hybrid quantum-classical generative models for quantum chemistry tasks.