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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.07310 |
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| _version_ | 1866909182547984384 |
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| author | Nenno, Dennis Michael Caspari, Adrian |
| author_facet | Nenno, Dennis Michael Caspari, Adrian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_07310 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Dynamic Optimization on Quantum Hardware: Feasibility for a Process Industry Use Case Nenno, Dennis Michael Caspari, Adrian Optimization and Control Emerging Technologies 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. |
| title | Dynamic Optimization on Quantum Hardware: Feasibility for a Process Industry Use Case |
| topic | Optimization and Control Emerging Technologies |
| url | https://arxiv.org/abs/2311.07310 |