In this paper, we introduce LOTUS (Layer-Ordered Temporally-Unified Schedules), which is a framework that restructures QAOA from a high-dimensional, chaotic search into a low-dimensional dynamical system. By replacing independent layer-wise angles with a Hybrid Fourier-Autoregressive (HFA) mapping, LOTUS enforces global temporal coherence while maintaining local flexibility. LOTUS consistently outperforms standard optimizers, achieving up to a 27.2% improvement in expectation values over L-BFGS-B and 20.8% compared with COBYLA. Besides, our proposed method drastically reduces computational costs, requiring over 90% fewer iterations than methods like Powell or SLSQP.
Cite
@article{arxiv.2601.07851,
title = {LOTUS: Layer-ordered Temporally Unified Schedules For Quantum Approximate Optimization Algorithms},
author = {Phuong-Nam Nguyen},
journal= {arXiv preprint arXiv:2601.07851},
year = {2026}
}