DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search

Reinforcement-learning-based quantum architecture search is expensive because it repeatedly runs costly quantum simulations (VQE) after every circuit change, even though circuit construction itself is fully deterministic. DreamQAS improves efficiency by only learning to predict the expensive post-simulation feedback, using an ensemble model for uncertainty-aware planning and selective real verification. It achieves the lowest energy error on most molecular tasks while needing far fewer real quantum evaluations --- up to 10x fewer in some cases. This is valuable for quantum computing research, particularly in designing efficient quantum circuits for chemistry and materials simulation under limited computational budgets. Authors: Jiayang Niu, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Paper: https://arxiv.org/abs/2607.29491v1