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 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. [ABSTRACT FROM AUTHOR]