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A Distributed Quantum Approximate Optimization Algorithm For Unit Commitment — arXiv:2608.04159

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.04159
License (as archived): CC0-1.0
Archived: 2026-10-31T11:25:35.682Z
SHA-256 of the archived text: 9abe99480b2b83c431d698c6bee89245630337cff1f3278064eb711cb45cd26c
Stable URL of this page: https://metraipolis.com/documento/dc_4b4e190efbcc

How to cite

A Distributed Quantum Approximate Optimization Algorithm For Unit Commitment — arXiv:2608.04159 — arXiv (en), https://arxiv.org/abs/2608.04159 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4b4e190efbcc (Metraipolis archive dc_4b4e190efbcc, sha256 9abe99480b2b83c431d698c6bee89245630337cff1f3278064eb711cb45cd26c)

BibTeX

@misc{dc4b4e190efbcc,
  title = {A Distributed Quantum Approximate Optimization Algorithm For Unit Commitment — arXiv:2608.04159},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.04159},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4b4e190efbcc; sha256 9abe99480b2b83c431d698c6bee89245630337cff1f3278064eb711cb45cd26c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.04159. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper presents a distributed quantum approximate optimization algorithm (DQAOA)-enabled three-block alternating direction method of multipliers (ADMM) framework for unit commitment (UC). The relaxed commitment and dispatch variables are solved in a continuous quadratic programming block, while the binary commitment block is formulated as a quadratic unconstrained binary optimization (QUBO) problem. The DQAOA interface allows this QUBO to be solved using brute-force enumeration, monolithic QAOA, or distributed QAOA, while the remaining ADMM updates are kept unchanged. In the distributed mode, the logical commitment qubits are allocated across multiple capacity-constrained quantum processing units (QPU), avoiding the requirement that the complete binary problem fits on a single device. The framework is evaluated on a five-unit UC instance containing 15 binary variables. All three solver modes reduce the ADMM primal residual below a certain tolerance and recover the same commitment schedule, dispatch, and operating cost. The results demonstrate solution consistency across the three solver modes and the multi-QPU capacity accommodation provided by the distributed QAOA method.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-08-06 in cs.DC).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2608.04159 — 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 2026-10-31T11:14:26.129Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-08-06?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).