{
  "abstract": "Quantum neural networks (QNNs) are typically built from variational quantum circuits (VQCs), which are limited by local measurements. Adaptive non-local observables (ANO) address this by jointly optimizing circuit parameters and multi-qubit measurements. However, existing ANO-based VQCs learn only a single static observable that remains invariant across all inputs. We propose QFWP-ANO, a novel architecture which employs a classical hypernetwork to dynamically program VQC parameters and/or non-local observables conditioned on each input. On multivariate time-series forecasting across four ETT datasets, QFWP-ANO achieves the lowest MSE in 16 of 20 settings and second-lowest in the remaining four, surpassing ANO-based and other strong baselines. On reinforcement learning tasks, QFWP-ANO consistently surpasses ANO-VQCs. Our results establish input-conditioned ANO as an effective approach for enhancing QNNs.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.18655",
  "author_total": 3,
  "authors": [
    {
      "name": "Yu-Ting Lee"
    },
    {
      "name": "Samuel Yen-Chi Chen"
    },
    {
      "name": "Huan-Hsin Tseng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9wfk5xky3nr2m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.18655",
  "fulltext_url": null,
  "id": "9wfk5xky3nr2m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.18655",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7213509769",
  "pdf_url": "https://arxiv.org/pdf/2609.18655",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9wfk5xky3nr2m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "2609.18655",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.18655"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Learning to Program Adaptive Non-Local Observables for Machine Learning",
  "title_text": "Learning to Program Adaptive Non-Local Observables for Machine Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/9wfk5xky3nr2m",
  "venue": "arXiv",
  "versions": []
}