Dynamic optimization on quantum hardware: Feasibility for a process industry use case

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 surmou...

Full description

Saved in:
Bibliographic Details
Published in:Computers & chemical engineering Vol. 186; p. 108704
Main Authors: Nenno, Dennis M., Caspari, Adrian
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.07.2024
Subjects:
Chemical reactor
Dynamic optimization
Full discretization
Process industry
Quadratic unconstrained binary optimization problem
Quantum annealing
Quantum computing
Full discretization
Quantum computing
Chemical reactor
Dynamic optimization
Quantum annealing
Process industry
Quadratic unconstrained binary optimization problem
ISSN:0098-1354, 1873-4375
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 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. •Maps dynamic optimization problem for a CSTR model onto a QUBO framework for quantum annealing.•Compares the performance of quantum annealers with classical optimization solvers.•Reports on the application of D-Wave’s hybrid solver to manage problem embedding.•Illuminates the limitations of current quantum hardware for complex optimization tasks.•Suggests future directions for algorithm development compatible with quantum systems.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2024.108704