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Quantum annealing for global optimization in Chemical Engineering

  • Classical computing has experienced rapid growth in computational power, driven by the need to address increasingly complex industrial problems. The domain of global optimization plays a vital role in various applications, including optimal control, scheduling and assignment problems, or machine learning parameter selection. Currently, deterministic optimization techniques based on classical computing fail to deliver reasonable solutions within practical time constraints. Consequently, reliance on heuristic methods becomes common, albeit with no guarantee of solution quality. While ongoing algorithmic refinements lead to gradual enhancements in global optimization, they do little to address the fundamental issue of computational intractability. With the advent of quantum computing, a natural question arises: Can quantum methods offer advancements beyond classical approaches? Quantum annealing emerges as a promising subfield within quantum computing, necessitating the reformulation of problems as quadratic unconstrained binaryClassical computing has experienced rapid growth in computational power, driven by the need to address increasingly complex industrial problems. The domain of global optimization plays a vital role in various applications, including optimal control, scheduling and assignment problems, or machine learning parameter selection. Currently, deterministic optimization techniques based on classical computing fail to deliver reasonable solutions within practical time constraints. Consequently, reliance on heuristic methods becomes common, albeit with no guarantee of solution quality. While ongoing algorithmic refinements lead to gradual enhancements in global optimization, they do little to address the fundamental issue of computational intractability. With the advent of quantum computing, a natural question arises: Can quantum methods offer advancements beyond classical approaches? Quantum annealing emerges as a promising subfield within quantum computing, necessitating the reformulation of problems as quadratic unconstrained binary optimization (QUBO) problems. In this contribution, a novel approach is introduced to transform relevant problems in Chemical Engineering into QUBO at two distinct levels of granularity. Subsequently, these problem systems are embedded within virtual quantum machines employing two different architectures. Additionally, a comparative analysis is performed, wherein the same problem is solved utilizing both classical global optimization methods based on metaheuristics and a hypothetical quantum annealer. The findings indicate that annealing-based solving methods exhibit the most potential, indicating their applicability to the transformed formulation Chemical Engineering problems.show moreshow less

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Metadaten
Author: Bogdan DorneanuORCiD, Eduardo Nolasco, Vassilios S. Vassiliadis, Harvey Arellano-GarciaORCiD
URL:https://dechema.de/PAAT2023_Prg/_/__Progr_PAAT_2023_final.pdf
Title of the source (German):Jahrestreffen "Prozess-, Apparate- und Anlagentechnik" - PAAT 2023, Frankfurt am Main
Document Type:Image (poster)
Language:English
Year of publication:2023
Contributing Corporation:DECHEMA
First Page:15
Faculty/Chair:Fakultät 3 Maschinenbau, Elektro- und Energiesysteme / FG Prozess- und Anlagentechnik
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