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Mathematics > Optimization and Control
Title: Dynamic Optimization on Quantum Hardware: Feasibility for a Process Industry Use Case
(Submitted on 13 Nov 2023 (v1), last revised 26 Apr 2024 (this version, v3))
Abstract: The quest for real-time dynamic optimization solutions in the process industry represents a formidable computational challenge, particularly within the realm of applications like model-predictive control, where rapid and reliable computations are critical. Conventional methods can struggle to surmount the complexities of such tasks. Quantum computing and quantum annealing emerge as \textit{avant-garde} contenders to transcend conventional computational constraints. We convert a dynamic optimization problem, {characterized by an optimization problem with a system of differential-algebraic equations embedded}, into a Quadratic Unconstrained Binary Optimization problem, enabling quantum computational approaches. The empirical findings synthesized from classical methods, simulated annealing, quantum annealing via D-Wave's quantum annealer, and hybrid solver methodologies, illuminate the intricate landscape of computational prowess essential for tackling complex and high-dimensional dynamic optimization problems. Our findings suggest that while quantum annealing is a maturing technology that currently does not outperform state-of-the-art classical solvers, continuous improvements could eventually aid in increasing efficiency within the chemical process industry.
Submission history
From: Adrian Caspari [view email][v1] Mon, 13 Nov 2023 13:03:37 GMT (1972kb,D)
[v2] Wed, 15 Nov 2023 14:50:04 GMT (1972kb,D)
[v3] Fri, 26 Apr 2024 08:55:57 GMT (2616kb,D)
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