# Dynamic Optimization on Quantum Hardware: Feasibility for a Process Industry Use Case

arXiv: https://arxiv.org/abs/2311.07310 · HTML: https://mlsanity.com/paper/2311.07310/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2023.

## Built on by (most cited first, 1 of 1)

- [Classi|Qrangle Towards a Translation Framework To Bridge The Classical-Quantum Programming Gap](https://mlsanity.com/paper/2406.06764/) (2024)

## Abstract (arXiv)

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 textitavant-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.