[2311.10433] Task Scheduling Optimization with Direct Constraints from a Tensor Network Perspective Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2311.10433 (quant-ph) [Submitted on 17 Nov 2023 ( v1 ), last revised 28 Apr 2026 (this version, v4)] Title: Task Scheduling Optimization with Direct Constraints from a Tensor Network Perspective Authors: Alejandro Mata Ali , Iñigo Perez Delgado , Beatriz García Markaida , Aitor Moreno Fdez. de Leceta View a PDF of the paper titled Task Scheduling Optimization with Direct Constraints from a Tensor Network Perspective, by Alejandro Mata Ali and 2 other authors View PDF HTML (experimental) Abstract: This work presents a novel method for task optimization in industrial plants using quantum-inspired tensor network technology. This method obtains the best possible combination of tasks on a set of machines with directed constraints while minimizing the total execution cost. With this method, an exact and explicit solution of the problem is provided. This algorithm constructs a tensor network representation of the tensor which provides the solution of the problem. This method is improved in order to reduce the computational complexity of the solution computation, using problem preprocessing, new techniques of condensation of logical constraints, optimization of the value determination technique with previously calculated results, reuse of intermediate computations, and iterative relations for constraints. Three algorithms for computation are presented: the main algorithm, the iterative algorithm which adds only the minimal amount of necessary constraints, and the genetic algorithm which combines the iterative algorithm with basic genetic algorithms. Finally, a simple version of both algorithms was implemented, and their performance was tested, all publicly available. Comments: 15 pages, 15 figures, improved version, with better theorem demonstrations Subjects: Quantum Physics (quant-ph) ; Emerging Technologies (cs.ET) MSC classes: 68Q12, 15A69, 90C27 ACM classes: G.1.3; G.2.1 Cite as: arXiv:2311.10433 [quant-ph] (or arXiv:2311.10433v4 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2311.10433 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Alejandro Mata Ali [ view email ] [v1] Fri, 17 Nov 2023 10:10:46 UTC (60 KB) [v2] Thu, 20 Jun 2024 10:39:09 UTC (68 KB) [v3] Mon, 4 Aug 2025 19:20:31 UTC (243 KB) [v4] Tue, 28 Apr 2026 18:26:03 UTC (239 KB) Full-text links: Access Paper: View a PDF of the paper titled Task Scheduling Optimization with Direct Constraints from a Tensor Network Perspective, by Alejandro Mata Ali and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2023-11 Change to browse by: cs cs.ET References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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