Towards Efficient Function Optimisation with Quantum–Classical Hybrid Search

Finding the global minimum of a function with many local extrema is well known to be a challenging computational task. We propose a hybrid quantum–classical algorithm for this optimisation problem, based on a combination of Grover quantum search and the classical Newton–Raphson method. The approach is tailored to situations with a large number of false minima. The main idea is to use quantum search to efficiently locate the vicinity of the true optimum, after which a classical algorithm can solve the problem in only a few iterations. We specifically address the nontrivial question of choosing the discretisation of the function domain, which is critical for quantum approaches to optimisation of continuous functions. While we do not expect that this method will always be superior, we identify a paradigmatic case of a function for which we demonstrate that, remarkably, our hybrid method quadratically outperforms a purely classical approach (measuring performance by the number of function/oracle calls). We evaluate key performance metrics of the algorithm in numerical experiments. We furthermore discuss its possible extensions as well as the potential relevance of hybrid approaches in the context of quantum cognition.

# Science # Astrophysics # Physics # quantum computing # continuous function optimisation # Grover’s algorithm # Newton–Raphson’s method

Mansur Ziiatdinov, Fedor Levkovich-Maslyuk, Emmanuel M. Pothos (2026). "Towards Efficient Function Optimisation with Quantum–Classical Hybrid Search". Entropy. https://doi.org/10.3390/e28101060