LOTUS: Layer-ordered Temporally Unified Schedules For Quantum Approximate Optimization Algorithms

TL;DR

LOTUS introduces a novel framework for QAOA that transforms the optimization landscape into a low-dimensional dynamical system, significantly improving performance and reducing computational costs.

Contribution

This paper presents LOTUS, a new scheduling framework using a Hybrid Fourier-Autoregressive mapping to enhance QAOA optimization efficiency and effectiveness.

Findings

Achieves up to 27.2% improvement in expectation values.

Reduces computational costs by over 90% in iterations.

Outperforms standard optimizers like L-BFGS-B and COBYLA.

Abstract

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.