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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation — arXiv:2608.05819

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.05819
License (as archived): CC0-1.0
Archived: 2026-10-31T10:53:58.823Z
SHA-256 of the archived text: d9521b53329fdd3adff549faefd9d5b6b83fd1b9d4190336f7e35b7d4953cda0
Stable URL of this page: https://metraipolis.com/documento/dc_ef409e4de3ed

How to cite

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation — arXiv:2608.05819 — arXiv (en), https://arxiv.org/abs/2608.05819 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ef409e4de3ed (Metraipolis archive dc_ef409e4de3ed, sha256 d9521b53329fdd3adff549faefd9d5b6b83fd1b9d4190336f7e35b7d4953cda0)

BibTeX

@misc{dcef409e4de3ed,
  title = {Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation — arXiv:2608.05819},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.05819},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ef409e4de3ed; sha256 d9521b53329fdd3adff549faefd9d5b6b83fd1b9d4190336f7e35b7d4953cda0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.05819. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Classical simulation remains essential for developing and validating quantum algorithms, but its cost grows rapidly with circuit size. Tensor-network contraction can reduce this cost by exploiting circuit structure, although its efficiency depends strongly on the chosen contraction plan. On GPUs, plans with similar theoretical complexity may perform very differently because execution also depends on parallelism, reduction structure, memory traffic, and contraction geometry. We present a learning-to-rank framework for selecting efficient contraction plans before executing them. Each plan is represented by structural features derived directly from its sequence of pairwise contractions, and gradient-boosted rankers are trained from GPU measurements using listwise and pairwise objectives. We evaluate the resulting models on diverse circuit families, using separate in-distribution and circuit-family-shift test sets, and compare them with random and MinFill-based baselines. The learned rankers generally identify better plans, with the listwise model providing the strongest overall decision quality. We also study backend shift by comparing empirical plan orderings on two GPU architectures and evaluating the source-trained models on the second device without retraining. The rankings remain substantially, though not perfectly, stable across GPUs, and the models retain useful decision quality. These results support Learning to Rank as a practical way to reduce contraction-plan search, while showing that performance remains partly backend dependent.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-08-07 in quant-ph).
Categorie arXiv: Machine Learning (cs.LG) ; Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF); Quantum Physics (quant-ph).
Pagina del lavoro: https://arxiv.org/abs/2608.05819 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T10:40:45.645Z dalla pagina https://arxiv.org/catchup/quant-ph/2026-08-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).