{
  "annotation_id": "b78dab61-1b06-416c-8bd0-61a8ba91e145",
  "date_created": "2026-02-17T05:53:12.593000Z",
  "date_modified": "2026-02-17T05:53:12.593000Z",
  "file_hash": "6f4a40be0281508ff4c954f0a0d8fecfa81cfee5f88918d3f1396239d41d48a1",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.07851",
    "authors": [
      "Phuong-Nam Nguyen"
    ],
    "categories": [
      "quant-ph"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "title": "LOTUS: Layer-ordered Temporally Unified Schedules For Quantum Approximate Optimization Algorithms",
    "url": "https://arxiv.org/abs/2601.07851",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "8dae6f80-ad2a-462a-b4bd-004f936cf3c1",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}